01319nas 2200385- 450 991014185430332120190404054725.92330-5398(OCoLC)855975029(CKB)2670000000416324(CONSER)--2013203206(EXLCZ)99267000000041632420130819a20139999 --- aengur|||||||||||txtrdacontentcrdamediacrrdacarrierIntegritasChestnut Hill, Massachusetts :Division of University Mission and Ministry, Boston College,[2013]-Issued in part.A publication of The Boston College roundtable...advancing the mission of Catholic Higher Education.2330-538X Catholic universities and collegesUnited StatesPeriodicalsCatholic universities and collegesfast(OCoLC)fst00849270United StatesfastPeriodicals.fastCatholic universities and collegesCatholic universities and colleges.377.8273Boston College.Division of University Mission and Ministry.JOURNAL9910141854303321Integritas2240576UNINA03613nam 22006495 450 991029922520332120250617170248.03-319-15726-410.1007/978-3-319-15726-9(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 miningStatisticsArtificial IntelligenceData Mining and Knowledge DiscoveryStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesArtificial intelligence.Data mining.Statistics.Artificial Intelligence.Data Mining and Knowledge Discovery.Statistics in 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