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Fast training of SVM with [beta]-neighbor editing.

Wan Zhang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 91-103). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction to Classification --- p.1 / Chapter 1.2 --- Problem Definition --- p.4 / Chapter 1.3 --- Major Contributions --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Fisher's Linear Discriminant --- p.8 / Chapter 2.2 --- Radial Basis Function Networks --- p.9 / Chapter 2.3 --- Decision Tree --- p.10 / Chapter 2.4 --- Nearest Neighbor --- p.12 / Chapter 2.5 --- Support Vector Machine --- p.13 / Chapter 2.5.1 --- Linear Separable Case --- p.14 / Chapter 2.5.2 --- Non Linear-separable Case --- p.15 / Chapter 2.5.3 --- Nonlinear Case --- p.18 / Chapter 2.5.4 --- Multi-class SVM --- p.19 / Chapter 2.5.5 --- RSVM --- p.21 / Chapter 2.6 --- Summary --- p.23 / Chapter 3 --- Computational Geometry --- p.25 / Chapter 3.1 --- Convex hull --- p.26 / Chapter 3.1.1 --- Separable Case --- p.26 / Chapter 3.1.2 --- Inseparable Case --- p.28 / Chapter 3.2 --- Proximity Graph --- p.32 / Chapter 3.2.1 --- Voronoi Diagram and Delaunay Triangulation --- p.32 / Chapter 3.2.2 --- Gabriel Graph and Relative Neighborhood Graph --- p.34 / Chapter 3.2.3 --- β-skeleton --- p.36 / Chapter 4 --- Data Editing --- p.39 / Chapter 4.1 --- Hart's Condensed Rule and Its Relatives --- p.39 / Chapter 4.2 --- Order-independent Subsets --- p.40 / Chapter 4.3 --- Minimal Size Training-set Consistent Subsets --- p.40 / Chapter 4.4 --- Proximity Graph Methods --- p.41 / Chapter 4.5 --- Comparing Results of Different Classifiers with Edited Dataset as the Training Set --- p.42 / Chapter 4.5.1 --- Time Complexity --- p.47 / Chapter 4.5.2 --- Editing Size of Training Data --- p.48 / Chapter 4.5.3 --- Accuracy --- p.50 / Chapter 4.5.4 --- Efficiency --- p.54 / Chapter 4.5.5 --- Summary --- p.58 / Chapter 5 --- Techniques Speeding Up Data Editing --- p.60 / Chapter 5.1 --- Parallel Computing --- p.61 / Chapter 5.1.1 --- Basic Idea of Parallel --- p.61 / Chapter 5.1.2 --- Details of Parallel Technique --- p.63 / Chapter 5.1.3 --- Comparing Effects of the Choice of Number of Threads on Efficiency --- p.64 / Chapter 5.2 --- Tree Indexing Structure --- p.67 / Chapter 5.2.1 --- R-tree and R*-tree --- p.67 / Chapter 5.2.2 --- SS-tree --- p.69 / Chapter 5.2.3 --- SR-tvee --- p.70 / Chapter 5.2.4 --- β-neighbor Algorithm Based on SR-tree Structure --- p.71 / Chapter 5.2.5 --- Pruning Search Space for β-neighbor Algorithm --- p.72 / Chapter 5.2.6 --- Comparing Results of Non-index Methods with Those of Methods with Indexing --- p.80 / Chapter 5.3 --- Combination of Parallelism and SR-tree Indexing Structure --- p.83 / Chapter 5.3.1 --- Comparing Results of Both Techniques Applied --- p.84 / Chapter 5.4 --- Summary --- p.87 / Chapter 6 --- Conclusion --- p.89 / Bibliography --- p.91

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324428
Date January 2003
ContributorsWan, Zhang., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, xiii, 103 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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