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| 020 | ▼a 9781107096394 (hbk.) | |
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| 090 | ▼a 006.31 ▼b F571m | |
| 100 | 1 | ▼a Flach, Peter A. |
| 245 | 1 0 | ▼a Machine learning : ▼b the art and science of algorithms that make sense of data / ▼c Peter Flach. |
| 260 | ▼a Cambridge ; ▼a New York : ▼b Cambridge University Press, ▼c 2012. | |
| 300 | ▼a xvii, 396 p. : ▼b col. ill. ; ▼c 25 cm. | |
| 504 | ▼a Includes bibliographical references (p. 367-381) and index. | |
| 505 | 0 0 | ▼g 1. ▼t The ingredients of machine learning -- ▼g 2. ▼t Binary classification and related tasks -- ▼g 3. ▼t Beyond binary classification -- ▼g 4. ▼t Concept learning -- ▼g 5. ▼t Tree models -- ▼g 6. ▼t Rule models -- ▼g 7. ▼t Linear models -- ▼g 8. ▼t Distance-based models -- ▼g 9. ▼t Probabilistic models -- ▼g 10. ▼t Features -- ▼g 11. ▼t Model ensembles -- ▼g 12. ▼t Machine learning experiments -- ▼g Epilogue: ▼t where to go from here. |
| 520 | 3 | ▼a 'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike. |
| 650 | 0 | ▼a Machine learning ▼v Textbooks. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 F571m | 등록번호 121234395 (11회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
정보제공 :
목차
Prologue: a machine learning sampler; 1. The ingredients of machine learning; 2. Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where to go from here; Important points to remember; Bibliography; Index.
정보제공 :