Home
Search results “Web mining with relational clustering math”
Facebook Friend Recommendation using Graph Mining | AI Case Study
 
04:33
Follow me on Facebook facebook.com/himanshu.kaushik.2590 Subscribe to our channel on youtube to get latest updates on Video lectures Our video lectures are helpful for examinations like GATE UGC NET ISRO DRDO BARCH OCES DCES DSSSB NIELIT Placement preparations in Computer Science and IES ESE for mechanical and Electronics. Get access to the most comprehensive video lectures call us on 9821876104/02 Or email us at [email protected] Visit Our websites www.gatelectures.com and www.ugcnetlectures.com For classroom coaching of UGC NET Computer Science or GATE Computer Science please call us on 9821876104 Real World Application of Artificial Intelligence in facebook using Graph mining. Get access to all the video lectures visit our website www.appliedaicourse.com Links of Our Demo lectures playlists Our Courses - https://goo.gl/pCZztL Data Structures - https://goo.gl/HrZE6J Algorithm Design and Analysis - https://goo.gl/hT2JDg Discrete Mathematics - https://goo.gl/QQ8A8D Engineering Mathematics - https://goo.gl/QGzMFv Operating System - https://goo.gl/pzMEb6 Theory of Computation - https://goo.gl/CPBzJZ Compiler Design - https://goo.gl/GhcLJg Quantitative Aptitude - https://goo.gl/dfZ9oD C Programming - https://goo.gl/QRNx54 Computer Networks - https://goo.gl/jYtsCQ Digital Logic - https://goo.gl/3iosMc Database Management System - https://goo.gl/84pCFD Computer Architecture and Organization - https://goo.gl/n9H69F Microprocessor 8085 - https://goo.gl/hz5bvv Artificial Intelligence - https://goo.gl/Y91rk2 Java to Crack OCJP and SCJP Examination - https://goo.gl/QHLKi7 C plus plus Tutorials - https://goo.gl/ex1dLC Linear Programming Problems - https://goo.gl/RnRHXH Computer Graphics - https://goo.gl/KaGsXs UNIX - https://goo.gl/9Le7sX UGC NET November examination video solutions - https://goo.gl/Wos193 NIELIT 2017 Question paper Solutions - https://goo.gl/w9QkaE NIELIT Exam Preparation Videos - https://goo.gl/cXMSyA DSSSB Video Lectures - https://goo.gl/f421JF ISRO 2017 Scientist SC paper Solution - https://goo.gl/bZNssE Computer Graphics - https://goo.gl/uWwtgw Number System Digital logic - https://goo.gl/7Q1vG1 Live Classroom Recordings - https://goo.gl/pB1Hvi Verbal Aptitude - https://goo.gl/oJKwfP Thermodynamics - https://goo.gl/BN5Gd6 Heat and Mass Transfer - https://goo.gl/Lg6DzN Pre and Post GATE Guidance - https://goo.gl/k5Ybnz GATE Preparation Tips by Kishlaya Das GATE AIR 37 - https://goo.gl/jfFWQp #GATE #UGCNET
Views: 1103 DigiiMento Education
mod01lec01
 
23:12
Views: 17258 Data Mining - IITKGP
008. Statistics of a random graph in the Bollobas-Borgs-Chayes-Riordan model - Evgeny Grechnikov
 
01:51:27
We study the model of directed random graphs introduced by Bollobas, Borgs, Chayes and Riordan: at each time step, either a new edge between two existing vertices is added or a new vertex with one incoming or outcoming edge is created. In each case, existing vertices are selected using preferential attachment. We significantly improve results on the distribution of incoming and outcoming degrees in that model. Moreover, we study the distribution of the random variable X(d_1,d_2) which is equal to the total number of edges of G(t) drawn from some vertex of out-degree d_1 to some vertex of in-degree d_2.
Mining Your Logs - Gaining Insight Through Visualization
 
01:05:04
Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 24954 GoogleTechTalks
Implementing a Neoteric Clustering approach in Wireless Sensor Networks using Spectral Clustering an
 
09:14
In recent years, one of the most popular modern clustering algorithms in various fields like Network Science applications, Data mining, Pattern Recognition, etc., are spectral clustering algorithms. Another area of research interest found much attractive recently are Wireless Sensor Networks(WSNs), consisting of low power, low-cost, and energy-constrained sensors employed to monitor and report a physical phenomenon to the sink node where the end-user can access the data. Some of the important challenges in WSNs are increasing network longevity and decreasing consumption of sensor energy. To handle these clustering algorithms can be utilized and use spectral graph theory in order to subdivide the network such that each cluster includes the highest inter-correlated sensors. In this presentation, we widely analyze the need and efficacy of use of Spectral Clustering algorithms from a network Graph partitioning point of view. To do this, some vital aspects of spectral clustering was studied; Also analysis and actual implementation of meaningful partition of simple data sets was employed. This was followed by deriving and subsequent implementations of two typical spectral clustering algorithms, namely, ratio-cuts and normalized-cuts thus providing sufficient insight by proposing experiments on large web-graphs and thereafter discussing/analyzing the results. Finally, using a neoteric approach called K-Way Spectral Clustering Algorithm in Wireless Sensor Network (KSCA-WSN) we can try to address its aforementioned challenges. Experimental results and subsequent simulations and observations attest to the fact that this implementation produces performance of better quality, they give a good approximation of the min-cut graph partitioning problem in terms of reducing the cut size and KSCA-WSNs help in effectively distributing the overall consumption of sensor energy as well as ensuring larger network lifetimes, taking into account quantitative as well as visual evaluations.
Views: 139 Sankalp Mohanty
GraphConnect SF 2015 / Graphs Are Feeding The World, Tim Williamson, Data Scientist, Monsanto
 
37:16
Graphs Are Feeding The World, Tim Williamson, Data Scientist, Monsanto
Views: 3246 Neo4j
Final Year Projects | Clustering Large Probabilistic Graphs
 
05:51
Final Year Projects | Clustering Large Probabilistic Graphs More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 223 ClickMyProject
Lecture - 34 Data Mining and Knowledge Discovery
 
54:46
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 132667 nptelhrd
Effective and Efficient Clustering Methods for Correlated Probabilistic Graphs
 
00:42
Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training...... ---------------------------------------------------------------- JAVA & .NET PROJECTS: Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc., EMBEDDED SYSTEMS: Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc., ROBOTICS PROJECTS: Mobile Robots, Service Robots, Industrial Robots, Defence Robots, Spy Robot, Artificial Robots, Automated Machine Control, Stair Climbing, Cleaning, Painting, Industry Security Robots, etc., MOBILE APPLICATION (ANDROID & J2ME): Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc., MECHANICAL PROJECTS: Auto Mobiles, Hydraulics, Robotics, Air Assisted Exhaust Breaking System, Automatic Trolley for Material Handling System in Industry, Hydraulics And Pneumatics, CAD/CAM/CAE Projects, Special Purpose Hydraulics And Pneumatics, CATIA, ANSYS, 3D Model Animations, etc., CONTACT US: ECWAY TECHNOLOGIES 15/1 Sathiyamoorthi Nagar, 2nd Cross, Thanthonimalai(Opp To Govt. Arts College) Karur-639 005. TamilNadu , India. Cell: +91 9894917187. Website: www.ecwayprojects.com | www.ecwaytechnologies.com Mail to: [email protected]
Views: 151 Ecway Karur
Ontological Logical Benefits
 
02:25
Why would you suggest that by using logic and reasoning, it provides a better argument for the existence of God than using empirical evidence?
Views: 100 David Webster
High scale in-database modeling in Greenplum with R
 
01:01:54
Greenplum is a massively parallel relational database platform. R is one of the top languages in the data scientist/applied statistician community. In this talk, I'll give an overview of how they work together, both with R on the desktop and as an embedded in-database analytics tool. It'll be a variation of a talk recently presented at the UseR 2012 Conference. Hong Ooi graduated from Macquarie University with a BEc in actuarial studies, then worked with NRMA Insurance/IAG in Sydney for many years. Completed a Masters in Applied Stats from Macquarie in 1997, and a PhD in statistics from ANU from 2000-2004. Displayed impeccable timing by switching jobs to St George Bank on the eve of the global financial crisis. Moved to Melbourne in 2009, before joining the Greenplum data science team in 2012.
Views: 1706 Jeromy Anglim
KDD2016 paper 276
 
02:02
Title: PTE: Enumerating Trillion Triangles On Distributed Systems Authors: Ha-Myung Park*, KAIST Sung-Hyon Myaeng, KAIST U Kang, Seoul National University Abstract: How can we enumerate triangles from an enormous graph with billions of vertices and edges? Triangle enumeration is an important task for graph data analysis with many applications including identifying suspicious users in social networks, detecting web spams, finding communities, etc. However, recent networks are so large that most of the previous algorithms fail to process them. Recently, several MapReduce algorithms have been proposed to address such large networks; however, they suffer from the massive shuffled data resulting in a very long processing time. In this paper, we propose PTE (Pre-partitioned Triangle Enumeration), a new distributed algorithm for enumerating triangles in enormous graphs by resolving the structural inefficiency of the previous MapReduce algorithms. PTE enumerates trillions of triangles in a billion scale graph by decreasing three factors: the amount of shuffled data, total work, and network read. Experimental results show that PTE provides up to 47 times faster performance than recent distributed algorithms on real world graphs, and succeeds in enumerating more than 3 trillion triangles on the ClueWeb12 graph with 6.3 billion vertices and 72 billion edges, which any previous triangle computation algorithm fail to process. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 250 KDD2016 video
DWM -  Benefits and Users of Data Warehouse
 
04:50
In this video, we cover the following topics- 1. Benefits of Data Warehouse 2. Users of Data Warehouse Link of previous video- https://youtu.be/556RfSpK5Qk Tutorial lecture by Anisha Lalwani
Views: 427 topNotch Tutorials
Data Academy Relational Transformations
 
00:36
Demonstration of the Award Winning SQL Server Data Warehouse Tool Data Academy
Views: 98 DataAcademy
SSDC clustering of PHP Payloads in Gephi
 
01:55
Generating the data for https://twitter.com/botnet_hunter/status/577305005805289472 Tools used overall: https://github.com/bwall/ssdc http://gephi.github.io/ http://sigmajs.org/ Result hosted at https://defense.ballastsecurity.net/static/graphs/index.html
Views: 257 Brian Wallace
Running Large Graph Algorithms: Evaluation of Current State-Of-the-Art and Lessons Learned
 
50:37
Google Tech Talk February 11, 2010 ABSTRACT Presented by Dr. Andy Yoo, Lawrence Livermore National Laboratory. Graphs have gained a lot of attention in recent years and have been a focal point in many emerging disciplines such as web mining, computational biology, social network analysis, and national security, just to name a few. These so-called scale-free graphs in the real world have very complex structure and their sizes already have reached unprecedented scale. Furthermore, most of the popular graph algorithms are computationally very expensive, making scalable graph analysis even more challenging. To scale these graph algorithms, which have different run-time characteristics and resource requirements than traditional scientific and engineering applications, we may have to adopt vastly different computing techniques than the current state-of-art. In this talk, I will discuss some of the findings from our studies on the performance and scalability of graph algorithms on various computing environments at LLNL, hoping to shed some light on the challenges in scaling large graph algorithms. Andy Yoo is a computer scientist in the Center for Applied Scientific Computing (CASC). His current research interests are scalable graph algorithms, high performance computing, large-scale data management, and performance evaluation. He has worked on the large graph problems since 2004. In 2005, he developed a scalable graph search algorithm and demonstrated it by searching a graph with billions of edges on IBM BlueGene/L, then the largest and fastest supercomputer. Andy was nominated for 2005 Gordon Bell award for this work. He is currently working on finding right combination of architecture, systems, and programming model to run large graph algorithms. Andy earned his Ph.D. degree in Computer Science and Engineering from the Pennsylvania State University in 1998. He joined LLNL in 1998. Andy is a member of the ACM, IEEE and the IEEE Computer Society, and SIAM.
Views: 19019 GoogleTechTalks
Advanced Business Intelligence Techniques 21: PageRank and MapReduce Exercises
 
01:24:15
Advanced Business Intelligence Techniques 21st lesson by professor Mauro Brunato at University of Trento.
Views: 531 ReactiveSearch
Final Year Projects | VChunkJoin: An Efficient Algorithm for Edit Similaerity Join
 
06:12
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 50 ClickMyProject
Lecture - 35 Data Mining and Knowledge Discovery Part II
 
58:00
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 42996 nptelhrd
DATABASE TUNING-INTRODUCTION
 
03:25
PLZ LIKE SHARE AND SUBSCRIBE
Understanding Big Data (and not die trying) by Dr Serge Plata (Head of Analytics, Home Retail)
 
32:50
talk given at IMA London branch 1st November 2016 http://www.ima.org.uk/activities/branches/london.cfm.html Abstract: There are so many terms in the analytics industry today, and no one really understands them well: Data science, data analysis (or is it analytics?), data architecture, web analytics, business intelligence, and management information; there are also many platforms from relational databases to Hadoop clusters, and programming languages like python, .NET, C# and F# (sounds like music!); not to mention all the statistical packages like (R, SPSS and STATA) and front end tools (like Pentaho, D3.js or Tableau). But what does this mean? Where do all of these elements fit in the data and analytics profession? In this talk I will explain what they are, how they work and how they interact with each other, but most importantly how businesses use them to take advantage of all these mathematical tools and make data-driven decisions. I will give real life examples on these tools and methods like machine learning, data mining and mathematical modelling and also how platforms are used. Dr. Plata did his doctoral research at Imperial College London before taking up a post as a research fellow at the University of Exeter. Prior to that, he did a degree and masters in mathematics, specializing firstly on differential and algebraic topology and moving then into spectral theory, homeomorphic dynamics and ergodic theory which classically fall into the applied fields like optimisation, game theory, and machine learning. Among other publications, he wrote a book under Peter Lang Publishers titled "Visions of Applied Mathematics" In terms of mathematical applications, Dr. Plata has extensive experience building, and developing analytics programmes as well as leading data projects and data science teams from FTSE100 companies to technology SMEs. He has worked mainly in the retail space including digital and mobile, pioneering on behaviour analytics, machine learning and big data. He is a fellow of the IMA and he currently heads the 'data science and advanced analytics' team at Home Retail.
Views: 461 IMAmaths
Ontology Development & Apps for Clinical & Biological Adverse Event Data Integration & Analysis
 
01:00:16
Yongqun ''Oliver" He, DVN, Ph.D. Dept. of Microbiology and Immunology Center for Computational Medicine and Bioinformatics and Comprehensive Cancer Center University of Michigan Medical Shoal
Views: 308 UTHealth SBMI
Developer Data Scientist – New Analytics Driven Apps Using Azure Databricks & Apache Spark | B116
 
43:49
This session gives an introduction to machine learning for developers who are new to data science, and it shows how to build end-to-end MLlib Pipelines in Apache Spark. It provides example code to personalize recommendations, score inbound leads, or do natural language processing in Scala and Python. See how to productionize machine learning pipelines to create richer, more useful applications.
Palestra Amedeo Napoli - Concept Lattices in Knowledge Discovery and Knowledge Engineering
 
01:09:13
In this talk, we will introduce Formal Concept Analysis (FCA) and concept lattices, which are partially ordered structures which are produced by FCA. The basic units in concept lattices are concepts which are composed of an extent, i.e. the set of objects which are instances of the concept, and of an intent, i.e. the set of attributes which constitutes the description of the concept. The basic data structure in FCA is a binary table, but plain FCA has two main extensions, namely ``Pattern Structures'' and ``Relational Concept Analysis'' (RCA). Pattern Structures are able to deal with complex data such as numbers, intervals, sequences, and graphs. RCA takes into account relational data, i.e. relations between objects. We will discuss the capabilities of FCA, Pattern Structures and RCA in some tasks related to knowledge discovery and knowledge representation. Knowledge discovery techniques based on FCA are well formalized and allow the design of concept lattices from binary and complex data. Then concept lattices provide a realistic basis for the organization of a knowledge base. We will also provide some details on applications in the classification of RDF data (web of data), information retrieval, text mining, and biclustering. -------------------------------------------------------------------------------- Amedeo Napoli is a computer scientist (directeur de recherche CNRS) leading the Orpailleur Research Team at the LORIA/Inria Laboratory in Nancy. His scientific interests are related to artificial intelligence and data science. More precisely, he works in knowledge discovery, in formal concept analysis and extensions, in knowledge engineering, and especially on the bridges existing between data and knowledge. Amedeo Napoli is involved in many research projects (international, national and industrial) and has authored or co-authored more than 200 international publications (see www.loria.fr/~napoli).
Views: 617 CIn UFPE
Database Lesson #8 of 8 - Big Data, Data Warehouses, and Business Intelligence Systems
 
01:03:13
Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 74407 Dr. Daniel Soper
A Fuzzy Self-Constructing Feature Clustering Algorithm For Text Classification - 2011
 
00:36
http://www.mtechprojects.com - A Fuzzy Self-Constructing Feature Clustering Algorithm For Text Classification - 2011 - MTechProjects.com offering final year academic projects, MS, ME, M.Tech, MCA, BE, B.Tech, and PhD Students, Mtech MS ME MEng M.Sc Projects in Hyderabad,bangalore,chennai and delhi,india http://www.mtechprojects.com https://www.mtechprojects.com/topics/electric-sensing-devices.html https://www.mtechprojects.com/topics/equivalent-circuits.html https://www.mtechprojects.com/topics/voltage-regulators.html https://www.mtechprojects.com/topics/hvdc-power-transmission.html https://www.mtechprojects.com/topics/power-semiconductor-devices.html https://www.mtechprojects.com/topics/computer-architecture.html https://www.mtechprojects.com/topics/algorithm-design-and-analysis.html https://www.mtechprojects.com/topics/delays.html https://www.mtechprojects.com/topics/hardware.html https://www.mtechprojects.com/topics/standards.html https://www.mtechprojects.com/topics/microprocessors.html https://www.mtechprojects.com/topics/prototypes.html https://www.mtechprojects.com/topics/squaring.html https://www.mtechprojects.com/topics/arithmetic-circuit.html https://www.mtechprojects.com/topics/digit-serial.html https://www.mtechprojects.com/topics/fixed-point.html https://www.mtechprojects.com/topics/higher-radix.html https://www.mtechprojects.com/topics/table-lookup.html https://www.mtechprojects.com/topics/field-programmable-gate-arrays.html
Views: 614 MTech Projects
TEXT MINING PROJECTS IN SINGAPORE
 
00:14
DOTNET PROJECTS,2013 DOTNET PROJECTS,IEEE 2013 PROJECTS,2013 IEEE PROJECTS,IT PROJECTS,ACADEMIC PROJECTS,ENGINEERING PROJECTS,CS PROJECTS,JAVA PROJECTS,APPLICATION PROJECTS,PROJECTS IN MADURAI,M.E PROJECTS,M.TECH PROJECTS,MCA PROJECTS,B.E PROJECTS,IEEE PROJECTS AT MADURAI,IEEE PROJECTS AT CHENNAI,IEEE PROJECTS AT COIMBATORE,PROJECT CENTER AT MADURAI,PROJECT CENTER AT CHENNAI,PROJECT CENTER AT COIMBATORE,BULK IEEE PROJECTS,REAL TIME PROJECTS,RESEARCH AND DEVELOPMENT,INPLANT TRAINING PROJECTS,STIPEND PROJECTS,INDUSTRIAL PROJECTS,MATLAB PROJECTS,JAVA PROJECTS,NS2 PROJECTS, Ph.D WORK,JOURNAL PUBLICATION, M.Phil PROJECTS,THESIS WORK,THESIS WORK FOR CS
Views: 42 STAR TECHNOLOGY
Text By the Bay 2015: Kang Sun, Teaching Machines to Read for Fun and Profit
 
40:47
Kang Sun from the R&D Machine Learning group will speak about Bloomberg’s current projects in the area of Machine Learning and Natural Language Processing, such as sentiment analysis of financial news, market impact predictions, question answering, etc. There will be a discussion of future directions and as well as a Q&A session. In this talk Kang Sun from the R&D Machine Learning group at Bloomberg will speak about current projects involving Machine Learning and applications such as Natural Language Processing. We will discuss the evolution and development of several key Bloomberg projects such as sentiment analysis, market impact prediction, novelty detection, social media monitoring, question answering and topic clustering. We will show that these interdisciplinary problems lie at the intersection of linguistics, finance, computer science and mathematics, requiring methods from signal processing, machine vision and other fields. Throughout, we will talk about practicalities of delivering machine learning solutions to problems of finance and highlight issues such as importance of appropriate problem decomposition, feature engineering and interpretability. There will be a discussion of future directions and applications of Machine Learning in finance as well as a Q&A session. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 344 FunctionalTV
Fast Nearest Neighbor Search With Keywords
 
10:03
ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2013 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2013 Dot Net: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2012 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2012 Dot Net: http://www.chennaisunday.com/ieee-2012-projects.html IEEE 2011 JAVA: http://www.chennaisunday.com/ieee-2011-java-projects.html IEEE 2011 DOT NET: http://www.chennaisunday.com/ieee-2011-projects.html IEEE 2010 JAVA: http://www.chennaisunday.com/ieee-2010-java-projects.html IEEE 2010 DOT NET: http://www.chennaisunday.com/ieee-2010-dotnet-projects.html Real Time APPLICATION: http://www.chennaisunday.com/softwareprojects.html Contact: 9566137117/ 044-42046569 -- *Contact * * P.Sivakumar MCA Director ChennaiSunday Systems Pvt Ltd Phone No: 09566137117 New No.82, 3rd Floor, Arcot Road, Kodambakkam, Chennai - 600 024. URL: www.chennaisunday.com Location: http://www.chennaisunday.com/mapview.html
Views: 2625 Shiva Kumar
Marko Rodriguez: Distributed Graph Analytics with Faunus
 
01:54:45
ECCO/GBI seminars 2nd series 2012-2013 Distributed Graph Analytics with Faunus May 31, 2013 Brussels, VUB Marko A. Rodriguez CEO, Aurelius LLC (http://thinkaurelius.com/) Ph.D. Computer Science Abstract and more information: http://ecco.vub.ac.be/?q=node/206
Part 11 - The NIEHS Exposure Science and the Exposome Webinar Series - Dr. Paul Juarez
 
01:11:17
Dr. Paul Juarez, Professor of Meharry Medical College, Vice Chair for Research, Department of Family and Community Medicine and Director of the Health Disparities Research Center of Excellence, discusses applying exposomics research approaches, methods and analytics in conducting population-based, health disparities research by providing an integrated health disparities conceptual frame that links external and internal exposures.
Views: 233 NIEHS
D2I - Efficient Association Discovery with Keyword-based Constraints on Large Graph Data
 
01:06:40
Abstract: In many domains, such as social networks, cheminformatics, bioinformatics, and health informatics, data can be represented naturally in graph model, with nodes being data entries and edges the relationships between them. The graph nature of these data brings opportunities and challenges to data storage and retrieval. In particular, it opens the doors to search problems such as semantic association discovery and semantic search. Our group studied the application requirements in these domains and find that discovering Constrained Acyclic Paths (CAP) is highly in demand, based on such studies, we define the CAP search problem and introduce a set of quantitative metrics for describing keyword-based constraints. In addition, we propose a series of algorithms to efficiently evaluate CAP queries on large scale graph data. In this talk, I will focus on two main aspects of our study: (1) what's CAP query and how to express CAP queries in a structured graph query language; and (2) how to efficiently evaluate CAP queries on large graph data. Bio: Professor Wu completed her Ph.D. in Computer Science from the University of Michigan, Ann Arbor. She earned her M.S. degree from IU Bloomington in December 1999 and an M.S./B.S. degree from Peking University, China. Dr. Wu completed research internships at IBM Almaden Research Center as well as Microsoft Research in 2002 and 2003. Prof. Wu joined IU in 2004, and is currently an Associate Professor of Computer Science, of the School of Informatics and Computing. She is one of the founders of the TIMBER, a high performance native XML database system capable of operating at large scale, through use of a carefully designed tree algebra and judicious use of novel access methods and optimizations techniques. Her research in the Timber project focused on XML data storage, query processing and optimization, especially cost-based query optimization. Prof. Wu's recent research at Indiana University involves algebra for XML queries, normalization, indexing and the security of XML data repositories, the storage and query of data on the Semantic Web and association discovery. Her past research projects include Access Control for XML (ACCESS), which focused on developing a framework for flexible access constraint specification, representation and efficient enforcement. Prof. Wu is also involved in research related to data integration, data mining, and knowledge discovery.
Views: 108 IU_PTI
Learning Rules for Anomaly Detection
 
01:00:54
Google Tech Talks August 10, 2007 ABSTRACT Anomaly detection has the potential to detect novel attacks, however, keeping the false alarm rate low is a challenging task. We discuss the LERAD algorithm that can learn concise and accurate rules for anomaly detection and demonstrate its effectiveness in network and host datasets. We will also discuss our recent work (KDD 07) on weighting versus pruning during the rule validation. If there is more time, I can also talk about: As mobile devices become more pervasive, we study the problem of spatial-temporal anomaly detection for identifying potential abuse. We discuss the STAD algorithm and show its performance on a cell phone dataset. Credits:...
Views: 4527 GoogleTechTalks
Data Science Essentials in Python Collect → ​Organize​ → ​Explore​ → ​Predict​ → Value​
 
02:55
Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you’re a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn’t want to memorize every function and option. Paperback and ebooks available from https://pragprog.com/titles/dzpyds
Views: 2778 PragProg
BigQuery, IPython, Pandas and R for data science, starring Pearson
 
28:22
In this Cloud episode of Google Developers Live, Felipe Hoffa hosts Pearson's Director of Data Science Collin Sellman, to celebrate Python Pandas release 0.13 and its Google BigQuery connector. Jacob Schaer and Sean Schaefer join them to demo its capabilities, and how Pearson uses data science to improve education.
Views: 25856 Google Developers
How To Install R Studio For R In Windows Tamil
 
04:16
How To Install R Program In Windows In Tamil How To Install R Studio For R In Windows Tamil Single and Multi Line Comment Line In R Program In Tamil Arithmetic Calculation In R Language In Tamil How To List and Remove variable In R Tamil Maths Function In R Program Tamil How To Use Help For R Program In R Studio Relational Operator In R Program For Free source code and Free Project Please visit : http://www.tutorjoes.com/ http://www.facebook.com/tutorjoes http://www.youtube.com/tutorjoes
Views: 647 Tutor-Joes Stanley
Towards Telesophy: Federating All the World' s Knowledge
 
01:05:47
Google Tech Talks July 11, 2007 ABSTRACT The Net is the global network, which enables users worldwide to interact with information. As new technologies mature, the functions of the protocols deepen, moving closer to cyberspace visions of "being one with all the world's knowledge". The Evolution of the Net has already proceeded from data transmission in the Internet to information retrieval in the Web. The global protocols are evolving towards knowledge navigation in the Interspace, moving from syntax to semantics. In the future, infrastructure will support analysis, for interactive correlations across knowledge sources. This moves closer towards "telesophy", (transparent infrastructure for)...
Views: 4265 Google
Final Year Projects | An Enhanced Fuzzy Similarity Based Concept Mining Model
 
06:30
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 57 myproject bazaar
Evaluating Similarity Measures: A Large-Scale Study in Orkut Social Network
 
48:36
Google TechTalk June 21, 2006 Ellen Spertus is a Software Engineer at Google and an Associate Professor of Computer Science at Mills College, where she directs the graduate program in Interdisciplinary Computer Science. She earned her bachelor's, master's, and doctoral degrees from MIT, and has done research in parallel computing, text classification, information retrieval, and online communities. She is also known for her work on women and computing and various odd adventures, which have led to write-ups in The Weekly World News and other fine publications. ABSTRACT As online information services grow, so does the need and opportunity for automated tools to help users find information of interest to them. One such method is collaborative filtering, which makes recommendations to users based on their collective past behavior. We performed an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network, as well as observing interesting social issues that arise in recommending communities within a real social network. Google engEDU
Views: 270 GoogleTalksArchive
HCDF: A Hybrid Community Discovery Framework
 
58:00
Google Tech Talk March 11, 2010 ABSTRACT Presented by Tina Eliassi-Rad. We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework ), can effectively incorporate hints from a number of other community detection algorithms and produce results that outperform the constituent parts. We describe two HCDF-based approaches which are: (1) effective, in terms of link prediction performance and robustness to small perturbations in network structure; (2) consistent, in terms of effectiveness across various application domains; (3) scalable to very large graphs; and (4) nonparametric. Our extensive evaluation on a collection of diverse and large real-world graphs, with millions of links, show that our HCDF-based approaches (a) achieve up to 0.22 improvement in link prediction performance as measured by area under ROC curve (AUC), (b) never have an AUC that drops below 0.91 in the worst case, and (c) find communities that are robust to small perturbations of the network structure as defined by Variation of Information (an entropy-based distance metric). Dr. Tina Eliassi-Rad, Lawrence Livermore National Laboratory http://people.llnl.gov/eliassirad1 Tina Eliassi-Rad (http://eliassi.org) is a computer scientist and principal investigator at the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. She will join the faculty at the Department of Computer Science at Rutgers University in Fall 2010. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research interests include data mining, machine learning, and artificial intelligence. Her work has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, and complex networks. She serves as an action editor for the Data Mining and Knowledge Discovery Journal.
Views: 4319 GoogleTechTalks
7160 Group Presentation - Semantic Models for Multimedia Database Searching and Browsing
 
22:19
Sandy Altucher, Jasna Cakmak, Sumandeep Guram, Anita Millers, Sarah O’Brien, and Jordan Wright
Views: 254 Jasna Cakmak
Active Learning for Ranking through Expected Loss Optimization
 
01:05
Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training...... ---------------------------------------------------------------- JAVA, .NET, NS2, MATLAB PROJECTS: Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc., EMBEDDED SYSTEMS: Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc., ROBOTICS PROJECTS: Mobile Robots, Service Robots, Industrial Robots, Defence Robots, Spy Robot, Artificial Robots, Automated Machine Control, Stair Climbing, Cleaning, Painting, Industry Security Robots, etc., MOBILE APPLICATION (ANDROID & J2ME): Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc., MECHANICAL PROJECTS: Auto Mobiles, Hydraulics, Robotics, Air Assisted Exhaust Breaking System, Automatic Trolley for Material Handling System in Industry, Hydraulics And Pneumatics, CAD/CAM/CAE Projects, Special Purpose Hydraulics And Pneumatics, CATIA, ANSYS, 3D Model Animations, etc., CONTACT US: ECWAY TECHNOLOGIES 23/A, 2nd Floor, SKS Complex, OPP. Bus Stand, Karur-639 001. TamilNadu , India.Cell: +91 9894917187. Website: www.ecwayprojects.com | www.ecwaytechnologies.com Mail to: [email protected]
Views: 39 Ecway Karur
ODSC West 2015 | Chris Colburn - "How Big is Too Big? Anomaly Detection at Netflix"
 
37:58
Abstract: For web-scale consumer companies like Netflix the data flowing through our infrastructure changes daily. Typically these changes are subtle and have no material impact on downstream process, but occasionally there is a big change that surprises everyone (especially the engineer that pushed the code). At Netflix we’re working to push anomaly detection “up-stream.” That is to say, we’re building tools that automatically look at relations as they’re being built and inform owners that something is wrong. In order to accomplish this, we’ve had to build efficient ways of representing aggregate statistics about these pipelines that generalize to the different relational objects. In this talk, we will discuss: The architecture we’ve built to support the (many) anomaly detection use cases; the specifics of the math used to identify anomalies; and, the evidence that supports why this process works well for us. Bio: Chris Colburn leads the Personalization Data Engineering and Analytics team at Netflix. He has also released two open-source tools over the last two years: Robust Anomaly Detection (RAD) and ScorePMML. RAD finds anomalous data in periodic and semi-periodic time-series; and ScorePMML is used for rapidly deploying predictive models that have been built in R on massive volumes of Hadoop data. Prior to Netflix, Chris worked as a consultant for Teradata and Opera Solutions doing a hybrid of data engineering, analysis, and machine learning. He has a PhD in Mechanical Engineering from the University of California, San Diego where he leveraged Online Learning and Kalman Filters to track high-dimensional fluid simulations.
Views: 416 Open Data Science
Authors@Google: Ben Shneiderman
 
57:58
"Analyzing Social Media Networks with NodeXL" When information visualization is smoothly integrated with statistical techniques users can make important discoveries and bold decisions. Our 20-year history in coupling direct manipulation principles with dynamic queries, coordinated multiple windows, tree-maps, time-box selectors, and other innovations has produced academic and commercial success stories such as www.Spotfire.com and www.cs.umd.edu/hcil/treemap-history. Now we've turned to the difficult problem of network analysis and visualization. The free, open-source NodeXL (www.codeplex.com/nodexl) demonstrates novel approaches to importing network data (email, website, Facebook, Twitter, Flickr, etc.), applying metrics, performing clustering, and then giving rich controls over network layouts to support exploration and presentation. BEN SHNEIDERMAN (http://www.cs.umd.edu/~ben) is a Professor in the Department of Computer Science and Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/) at the University of Maryland. He was elected as a Fellow of the Association for Computing (ACM) in 1997, a Fellow of the American Association for the Advancement of Science (AAAS) in 2001, and a Member of the National Academy of Engineering in 2010. He received the ACM SIGCHI Lifetime Achievement Award in 2001.
Views: 4110 Talks at Google
Mining User Queries with Markov Chains: Application to Online Image Retrieval
 
02:46
Mining User Queries with Markov Chains: Application to Online Image Retrieval +91-9994232214,8144199666, [email protected], www.chennaibox.com , www.ieee-projects-chennai.com, www.ieeeprojectspondicherry.com
Yelawolf - Johnny Cash
 
04:16
Get Yelawolf's "Love Story" - http://smarturl.it/YelaLoveStory Sign up for updates: http://smarturl.it/Yelawolf.News Music video by Yelawolf performing Johnny Cash. (C) 2015 Interscope Records http://www.vevo.com/watch/USUV71400880 Best of Yelawolf: https://goo.gl/vy7NZQ Subscribe here: https://goo.gl/ynkVDL
Views: 10466872 YelawolfVEVO