logo
logo
x
바코드검색
BOOKPRICE.co.kr
책, 도서 가격비교 사이트
바코드검색

인기 검색어

실시간 검색어

검색가능 서점

도서목록 제공

Graphical Models: Representations for Learning, Reasoning and Data Mining

Graphical Models: Representations for Learning, Reasoning and Data Mining (Hardcover, 2)

Christian Borgelt (지은이)
John Wiley & Sons Inc
263,630원

일반도서

검색중
서점 할인가 할인률 배송비 혜택/추가 실질최저가 구매하기
197,720원 -25% 0원
5,940원
191,780원 >
yes24 로딩중
교보문고 로딩중
notice_icon 검색 결과 내에 다른 책이 포함되어 있을 수 있습니다.

중고도서

검색중
서점 유형 등록개수 최저가 구매하기
로딩중

eBook

검색중
서점 정가 할인가 마일리지 실질최저가 구매하기
로딩중

책 이미지

Graphical Models: Representations for Learning, Reasoning and Data Mining
eBook 미리보기

책 정보

· 제목 : Graphical Models: Representations for Learning, Reasoning and Data Mining (Hardcover, 2) 
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9780470722107
· 쪽수 : 404쪽
· 출판일 : 2009-09-15

목차

Preface.

1 Introduction.

1.1 Data and Knowledge.

1.2 Knowledge Discovery and Data Mining.

1.3 Graphical Models.

1.4 Outline of this Book.

2 Imprecision and Uncertainty.

2.1 Modeling Inferences.

2.2 Imprecision and Relational Algebra.

2.3 Uncertainty and Probability Theory.

2.4 Possibility Theory and the Context Model.

3 Decomposition.

3.1 Decomposition and Reasoning.

3.2 Relational Decomposition.

3.3 Probabilistic Decomposition.

3.4 Possibilistic Decomposition.

3.5 Possibility versus Probability.

4 Graphical Representation.

4.1 Conditional Independence Graphs.

4.2 Evidence Propagation in Graphs.

5 Computing Projections.

5.1 Databases of Sample Cases.

5.2 Relational and Sum Projections.

5.3 Expectation Maximization.

5.4 Maximum Projections.

6 Naive Classifiers.

6.1 Naive Bayes Classifiers.

6.2 A Naive Possibilistic Classifier.

6.3 Classifier Simplification.

6.4 Experimental Evaluation.

7 Learning Global Structure.

7.1 Principles of Learning Global Structure.

7.2 Evaluation Measures.

7.3 Search Methods.

7.4 Experimental Evaluation.

8 Learning Local Structure.

8.1 Local Network Structure.

8.2 Learning Local Structure.

8.3 Experimental Evaluation.

9 Inductive Causation.

9.1 Correlation and Causation.

9.2 Causal and Probabilistic Structure.

9.3 Faithfulness and Latent Variables.

9.4 The Inductive Causation Algorithm.

9.5 Critique of the Underlying Assumptions.

9.6 Evaluation.

10 Visualization.

10.1 Potentials.

10.2 Association Rules.

11 Applications.

11.1 Diagnosis of Electrical Circuits.

11.2 Application in Telecommunications.

11.3 Application at Volkswagen.

11.4 Application at DaimlerChrysler.

A Proofs of Theorems.

A.1 Proof of Theorem 4.1.2.

A.2 Proof of Theorem 4.1.18.

A.3 Proof of Theorem 4.1.20.

A.4 Proof of Theorem 4.1.26.

A.5 Proof of Theorem 4.1.28.

A.6 Proof of Theorem 4.1.30.

A.7 Proof of Theorem 4.1.31.

A.8 Proof of Theorem 5.4.8.

A.9 Proof of Lemma .2.2.

A.10 Proof of Lemma .2.4.

A.11 Proof of Lemma .2.6.

A.12 Proof of Theorem 7.3.1.

A.13 Proof of Theorem 7.3.2.

A.14 Proof of Theorem 7.3.3.

A.15 Proof of Theorem 7.3.5.

A.16 Proof of Theorem 7.3.7.

B Software Tools.

Bibliography.

Index.

이 포스팅은 쿠팡 파트너스 활동의 일환으로,
이에 따른 일정액의 수수료를 제공받습니다.
이 포스팅은 제휴마케팅이 포함된 광고로 커미션을 지급 받습니다.
도서 DB 제공 : 알라딘 서점(www.aladin.co.kr)
최근 본 책