{"product_id":"modeling-the-internet-and-the-web-isbn-9780470849064","title":"Modeling the Internet and the Web","description":"Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet.  \u003cp\u003e Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level.\u003cbr\u003e  Takes a modern approach based on mathematical, probabilistic, and graphical modeling.\u003cbr\u003e  Provides an integrated presentation of theory, examples, exercises and applications.\u003cbr\u003e  Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web.\u003c\/p\u003e \u003cp\u003eInterdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.\u003c\/p\u003e \u003cp\u003e\"This book is fascinating!\" - David Hand (Imperial College, UK)\u003c\/p\u003e \u003cp\u003e\"This book provides an extremely useful introduction to the intellectually stimulating problems of data mining electronic business.\" - Andreas S. Weigend (Chief Scientist, Amazon.com)\u003cbr\u003e \u003c\/p\u003e  Preface.  \u003cp\u003e\u003cb\u003e1 Mathematical Background.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Probability and Learning from a Bayesian Perspective.\u003c\/p\u003e \u003cp\u003e1.2 Parameter Estimation from Data.\u003c\/p\u003e \u003cp\u003e1.3 Mixture Models and the Expectation Maximization Algorithm.\u003c\/p\u003e \u003cp\u003e1.4 Graphical Models.\u003c\/p\u003e \u003cp\u003e1.5 Classification.\u003c\/p\u003e \u003cp\u003e1.6 Clustering.\u003c\/p\u003e \u003cp\u003e1.7 Power-Law Distributions.\u003c\/p\u003e \u003cp\u003e1.8 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Basic WWW Technologies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Web Documents.\u003c\/p\u003e \u003cp\u003e2.2 Resource Identifiers: URI, URL, and URN.\u003c\/p\u003e \u003cp\u003e2.3 Protocols.\u003c\/p\u003e \u003cp\u003e2.4 Log Files.\u003c\/p\u003e \u003cp\u003e2.5 Search Engines.\u003c\/p\u003e \u003cp\u003e2.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Web Graphs.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Internet and Web Graphs.\u003c\/p\u003e \u003cp\u003e3.2 Generative Models for the Web Graph and Other Networks.\u003c\/p\u003e \u003cp\u003e3.3 Applications.\u003c\/p\u003e \u003cp\u003e3.4 Notes and Additional Technical References.\u003c\/p\u003e \u003cp\u003e3.5 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Text Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Indexing.\u003c\/p\u003e \u003cp\u003e4.2 Lexical Processing.\u003c\/p\u003e \u003cp\u003e4.3 Content-Based Ranking.\u003c\/p\u003e \u003cp\u003e4.4 Probabilistic Retrieval.\u003c\/p\u003e \u003cp\u003e4.5 Latent Semantic Analysis.\u003c\/p\u003e \u003cp\u003e4.6 Text Categorization.\u003c\/p\u003e \u003cp\u003e4.7 Exploiting Hyperlinks.\u003c\/p\u003e \u003cp\u003e4.8 Document Clustering.\u003c\/p\u003e \u003cp\u003e4.9 Information Extraction.\u003c\/p\u003e \u003cp\u003e4.10 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Link Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Early Approaches to Link Analysis.\u003c\/p\u003e \u003cp\u003e5.2 Nonnegative Matrices and Dominant Eigenvectors.\u003c\/p\u003e \u003cp\u003e5.3 Hubs and Authorities: HITS.\u003c\/p\u003e \u003cp\u003e5.4 PageRank.\u003c\/p\u003e \u003cp\u003e5.5 Stability.\u003c\/p\u003e \u003cp\u003e5.6 Probabilistic Link Analysis.\u003c\/p\u003e \u003cp\u003e5.7 Limitations of Link Analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Advanced Crawling Techniques.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Selective Crawling.\u003c\/p\u003e \u003cp\u003e6.2 Focused Crawling.\u003c\/p\u003e \u003cp\u003e6.3 Distributed Crawling.\u003c\/p\u003e \u003cp\u003e6.4 Web Dynamics.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Modeling and Understanding Human Behavior on the\u003c\/b\u003e \u003cb\u003eWeb.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Web Data and Measurement Issues.\u003c\/p\u003e \u003cp\u003e7.3 Empirical Client-Side Studies of Browsing Behavior.\u003c\/p\u003e \u003cp\u003e7.4 Probabilistic Models of Browsing Behavior.\u003c\/p\u003e \u003cp\u003e7.5 Modeling and Understanding Search Engine Querying.\u003c\/p\u003e \u003cp\u003e7.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Commerce on the Web: Models and Applications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Customer Data on theWeb.\u003c\/p\u003e \u003cp\u003e8.3 Automated Recommender Systems.\u003c\/p\u003e \u003cp\u003e8.4 Networks and Recommendations.\u003c\/p\u003e \u003cp\u003e8.5 Web Path Analysis for Purchase Prediction.\u003c\/p\u003e \u003cp\u003e8.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Mathematical Complements.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Graph Theory.\u003c\/p\u003e \u003cp\u003eA.2 Distributions.\u003c\/p\u003e \u003cp\u003eA.3 Singular Value Decomposition.\u003c\/p\u003e \u003cp\u003eA.4 Markov Chains.\u003c\/p\u003e \u003cp\u003eA.5 Information Theory.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: List of Main Symbols and Abbreviations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e  \"…I congratulate the authors on a very well-researched and well-written publication.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, August 2004, Vol. 46, No. 3)  \u003cp\u003e“…fascinating …I highly recommend this book…” (Short Book Reviews, August 2004)\u003c\/p\u003e \u003cp\u003e“…a very well-researched and well-written publication.” (Technometrics, August 2004) \u003c\/p\u003e  \u003cp\u003ePierre Baldi is a chancellor's professor of computer science at University of California Irvine and the director of its Institute for Genomics and Bioinformatics. Paolo Frasconi is the author of Modeling the Internet and the Web: Probabilistic Methods and Algorithms, published by Wiley.   The World Wide Web is growing in size at a remarkable rate.  It is a huge evolving system and its data are rife with uncertainties.  Probability and statistics are the fundamental mathematical tools that enable us to model, reason and infer meaningful results from such data.  \u003ci\u003eModelling the Internet and the Web\u003c\/i\u003e covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment.  It focuses on the information and application layers, as well as some of the merging properties of the Internet.  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides a comprehensive introduction to the modeling of the Internet and Web at the information  level.\u003c\/li\u003e \u003cli\u003eTakes a modern approach based on mathematical, probabilistic and graphical modeling.\u003c\/li\u003e \u003cli\u003eProvides an integrated presentation of theory, examples, exercies and applications.\u003c\/li\u003e \u003cli\u003eCovers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eInterdisciplinary in nature, \u003ci\u003eModeling the Internet and the Web\u003c\/i\u003e will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business and the social sciences.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989637873893,"sku":"NP9780470849064","price":113.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470849064.jpg?v=1761784908","url":"https:\/\/k12savings.com\/es\/products\/modeling-the-internet-and-the-web-isbn-9780470849064","provider":"K12savings","version":"1.0","type":"link"}