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Machine learning : the art and science of algorithms that make sense of data

Machine learning : the art and science of algorithms that make sense of data (11회 대출)

자료유형
단행본
개인저자
Flach, Peter A.
서명 / 저자사항
Machine learning : the art and science of algorithms that make sense of data / Peter Flach.
발행사항
Cambridge ;   New York :   Cambridge University Press,   2012.  
형태사항
xvii, 396 p. : col. ill. ; 25 cm.
ISBN
9781107096394 (hbk.) 1107096391 (hbk.) 9781107422223 (pbk.) 1107422221 (pbk.)
요약
'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.
내용주기
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. Model ensembles -- 12. Machine learning experiments -- Epilogue: where to go from here.
서지주기
Includes bibliographical references (p. 367-381) and index.
일반주제명
Machine learning --Textbooks.
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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 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

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 : Aladin

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