03847nam 22006495 450 991029922520332120200703025829.03-319-15726-410.1007/978-3-319-15726-9(CKB)3710000000412163(EBL)2094427(SSID)ssj0001501007(PQKBManifestationID)11878171(PQKBTitleCode)TC0001501007(PQKBWorkID)11520845(PQKB)10397003(DE-He213)978-3-319-15726-9(MiAaPQ)EBC2094427(PPN)186025831(EXLCZ)99371000000041216320150507d2015 u| 0engur|n|---|||||txtccrLearning with Partially Labeled and Interdependent Data /by Massih-Reza Amini, Nicolas Usunier1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (113 p.)Description based upon print version of record.3-319-15725-6 Includes bibliographical references and index.Introduction -- Introduction to learning theory -- Semi-supervised learning -- Learning with interdependent data.This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks. Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data. Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning.Artificial intelligenceData miningStatistics Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/S17020Artificial intelligence.Data mining.Statistics .Artificial Intelligence.Data Mining and Knowledge Discovery.Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.004006.3006.312519.5Amini Massih-Rezaauthttp://id.loc.gov/vocabulary/relators/aut1060956Usunier Nicolasauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299225203321Learning with Partially Labeled and Interdependent Data2516470UNINA