Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté. Image fixe : sans médiation
Auteur(s) : Schapire, Robert E.
Freund, Yoav (1961-....)
Titre(s) : Boosting [Texte imprimé] : foundations and algorithms / Robert E. Schapire and Yoav Freund
Publication : Cambridge (Mass.) : MIT press, cop. 2012
Description matérielle : 1 vol. (XV-526 p.) : ill. ; 24 cm
Collection : Adaptive computation and machine learning
Lien à la collection : Adaptive computation and machine learning
Comprend : 1. Introduction and Overview ; 1.1. Classification Problems and Machine Learning ;
1.2. Boosting ; 1.3. Resistance to Overfitting and the Margins Theory ; 1.4. Foundations
and Algorithms ; Summary ; Bibliographic Notes ; Exercises ; I. Core Analysis
; 2. Foundations of Machine Learning ; 2.1. Direct Approach to Machine Learning
; 2.2. General Methods of Analysis ; 2.3. Foundation for the Study of Boosting Algorithms
; Summary ; Bibliographic Notes ; Exercises ; 3. Using AdaBoost to Minimize Training
Error ; 3.1. Bound on AdaBoost's Training Error ; 3.2. Sufficient Condition for
Weak Learnability ; 3.3. Relation to Chernoff Bounds ; 3.4. Using and Designing
Base Learning Algorithms ; Summary ; Bibliographic Notes ; Exercises ; 4. Direct
Bounds on the Generalization Error ; 4.1. Using VC Theory to Bound the Generalization
Error ; 4.2. Compression-Based Bounds ; 4.3. Equivalence of Strong and Weak Learnability
; Summary ; Bibliographic Notes ; Exercises ; 5. Margins Explanation for Boosting's
Effectiveness ; 5.1. Margin as a Measure of Confidence ; 5.2. Margins-Based Analysis
of the Generalization Error ; 5.3. Analysis Based on Rademacher Complexity ; 5.4.
Effect of Boosting on Margin Distributions ; 5.5. Bias, Variance, and Stability ;
5.6. Relation to Support-Vector Machines ; 5.7. Practical Applications of Margins
; Summary ; Bibliographic Notes ; Exercises ; II. Fundamental Perspectives ;
6. Game Theory, Online Learning, and Boosting ; 6.1. Game Theory ; 6.2. Learning
in Repeated Game Playing ; 6.3. Online Prediction ; 6.4. Boosting ; 6.5. Application
to a "Mind-Reading" Game ; Summary ; Bibliographic Notes ; Exercises ; 7. Loss
Minimization and Generalizations of Boosting ; 7.1. AdaBoost's Loss Function ; 7.2.
Coordinate Descent ; 7.3. Loss Minimization Cannot Explain Generalization ; 7.4.
Functional Gradient Descent ; 7.5. Logistic Regression and Conditional Probabilities
; 7.6. Regularization ; 7.7. Applications to Data-Limited Learning ; Summary ;
Bibliographic Notes ; Exercises ; 8. Boosting, Convex Optimization, and Information
Geometry ; 8.1. Iterative Projection Algorithms ; 8.2. Proving the Convergence of
AdaBoost ; 8.3. Unification with Logistic Regression ; 8.4. Application to Species
Distribution Modeling ; Summary ; Bibliographic Notes ; Exercises ; III. Algorithmic
Extensions ; 9. Using Confidence-Rated Weak Predictions ; 9.1. Framework ; 9.2.
General Methods for Algorithm Design ; 9.3. Learning Rule-Sets ; 9.4. Alternating
Decision Trees ; Summary ; Bibliographic Notes ; Exercises ; 10. Multiclass Classification
Problems ; 10.1. Direct Extension to the Multiclass Case ; 10.2. One-against-All
Reduction and Multi-label Classification ; 10.3. Application to Semantic Classification
; 10.4. General Reductions Using Output Codes ; Summary ; Bibliographic Notes ;
Exercises ; 11. Learning to Rank ; 11.1. Formal Framework for Ranking Problems ;
11.2. Boosting Algorithm for the Ranking Task ; 11.3. Methods for Improving Efficiency
; 11.4. Multiclass, Multi-label Classification ; 11.5. Applications ; Summary ;
Bibliographic Notes ; Exercises ; IV. Advanced Theory ; 12. Attaining the Best
Possible Accuracy ; 12.1. Optimality in Classification and Risk Minimization ; 12.2.
Approaching the Optimal Risk ; 12.3. How Minimizing Risk Can Lead to Poor Accuracy
; Summary ; Bibliographic Notes ; Exercises ; 13. Optimally Efficient Boosting
; 13.1. Boost-by-Majority Algorithm ; 13.2. Optimal Generalization Error ; 13.3.
Relation to AdaBoost ; Summary ; Bibliographic Notes ; Exercises ; 14. Boosting
in Continuous Time ; 14.1. Adaptiveness in the Limit of Continuous Time ; 14.2.
BrownBoost ; 14.3. AdaBoost as a Special Case of BrownBoost ; 14.4. Experiments
with Noisy Data ; Summary ; Bibliographic Notes ; Exercises ; Appendix: Some Notation,
Definitions, and Mathematical Background ; A.1. General Notation ; A.2. Norms ;
A.3. Maxima, Minima, Suprema, and Infima ; A.4. Limits ; A.5. Continuity, Closed
Sets, and Compactness ; A.6. Derivatives, Gradients, and Taylor's Theorem ; A.7.
Convexity ; A.8. Method of Lagrange Multipliers ; A.9. Some Distributions and the
Central Limit Theorem.
Note(s) : Bibliogr. p. 501-510
Sujet(s) : Boosting (algorithmes)
Apprentissage supervisé (intelligence artificielle)
Indice(s) Dewey :
006.31 (23e éd.) = Apprentissage automatique (informatique)
Identifiants, prix et caractéristiques : ISBN 9780262017183. - ISBN 0262017180 (rel.)
Identifiant de la notice : ark:/12148/cb42719660v
Notice n° :
FRBNF42719660
(notice reprise d'un réservoir extérieur)
