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About this product
- Author(s)Zhi-Hua Zhou
- PublisherTaylor & Francis Ltd
- Date of Publication29/06/2012
- GenreComputing: Textbooks & Study Guides
- Country of PublicationUnited States
- ImprintChapman & Hall/CRC
- Content Note73 black & white illustrations, 2 black & white tables
- Weight476 g
- Width156 mm
- Height235 mm
- Spine14 mm
- Format DetailsUnsewn / adhesive bound
- Table Of ContentsIntroduction Basic Concepts Popular Learning Algorithms Evaluation and Comparison Ensemble Methods Applications of Ensemble Methods Boosting A General Boosting Procedure The AdaBoost Algorithm Illustrative Examples Theoretical Issues Multiclass Extension Noise Tolerance Bagging Two Ensemble Paradigms The Bagging Algorithm Illustrative Examples Theoretical Issues Random Tree Ensembles Combination Methods Benefits of Combination Averaging Voting Combining by Learning Other Combination Methods Relevant Methods Diversity Ensemble Diversity Error Decomposition Diversity Measures Information Theoretic Diversity Diversity Generation Ensemble Pruning What Is Ensemble Pruning Many Could Be Better Than All Categorization of Pruning Methods Ordering-Based Pruning Clustering-Based Pruning Optimization-Based Pruning Clustering Ensembles Clustering Categorization of Clustering Ensemble Methods Similarity-Based Methods Graph-Based Methods Relabeling-Based Methods Transformation-Based Methods Advanced Topics Semi-Supervised Learning Active Learning Cost-Sensitive Learning Class-Imbalance Learning Improving Comprehensibility Future Directions of Ensembles References Index Further Readings appear at the end of each chapter.
- Author BiographyZhi-Hua Zhou is a professor in the Department of Computer Science and Technology and the National Key Laboratory for Novel Software Technology at Nanjing University. Dr. Zhou is the founding steering committee co-chair of ACML and associate editor-in-chief, associate editor, and editorial board member of numerous journals. He has published extensively in top-tier journals, chaired many conferences, and won six international journal/conference/competition awards. His research interests encompass the areas of machine learning, data mining, pattern recognition, and multimedia information retrieval.
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