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[null,"1.","Record Nr.",null,"UNINA9910139896403321"]
[null,null,"Autore",null,"Hamel Lutz"]
[null,null,"Titolo",null,"Knowledge discovery with support vector machines / / Lutz Hamel"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ, : John Wiley & Sons, 2009"]
[null,null,"ISBN",null,"9786612345661"]
[{"$nulls":4},"9781118211038"]
[{"$nulls":4},"1118211030"]
[{"$nulls":4},"9781282345669"]
[{"$nulls":4},"1282345664"]
[{"$nulls":4},"9780470503065"]
[{"$nulls":4},"0470503068"]
[{"$nulls":4},"9780470503041"]
[{"$nulls":4},"0470503041"]
[null,null,"Descrizione fisica",null,"1 online resource (266 p.)"]
[null,null,"Collana",null,"Wiley series on methods and applications in data mining"]
[null,null,"Disciplina",null,"005.1"]
[null,null,"Soggetti",null,"Support vector machines"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer algorithms"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"KNOWLEDGE DISCOVERY WITH SUPPORT VECTOR MACHINES; CONTENTS; PREFACE; PART I; 1 WHAT IS KNOWLEDGE DISCOVERY?; 2 KNOWLEDGE DISCOVERY ENVIRONMENTS; 3 DESCRIBING DATA MATHEMATICALLY; 4 LINEAR DECISION SURFACES AND FUNCTIONS; 5 PERCEPTRON LEARNING; 6 MAXIMUM-MARGIN CLASSIFIERS; PART II; 7 SUPPORT VECTOR MACHINES; 8 IMPLEMENTATION; 9 EVALUATING WHAT HAS BEEN LEARNED; 10 ELEMENTS OF STATISTICAL LEARNING THEORY; PART III; 11 MULTICLASS CLASSIFICATION; 12 REGRESSION WITH SUPPORT VECTOR MACHINES; 13 NOVELTY DETECTION; APPENDIX A NOTATION; APPENDIX B TUTORIAL INTRODUCTION TO R"]
[{"$nulls":4},"B.1 Programming ConstructsB.2 Data Constructs; B.3 Basic Data Analysis; Bibliographic Notes; REFERENCES; INDEX"]
[null,null,null,"Sommario/riassunto","An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and "]
[{"$nulls":4},"mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-"]