Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Xiuwen Liu, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed June 15, 2004). Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (leaves 76-79).

Peng, Xiang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 98-115). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.3 / Chapter 1.2 --- Major Contributions --- p.6 / Chapter 1.3 --- Thesis Outline --- p.7 / Chapter 2 --- Background Study --- p.9 / Chapter 2.1 --- Content-based Image Retrieval --- p.9 / Chapter 2.1.1 --- Image Representation --- p.11 / Chapter 2.1.2 --- High Dimensional Indexing --- p.15 / Chapter 2.1.3 --- Image Retrieval Systems Design --- p.16 / Chapter 2.2 --- Relevance Feedback --- p.19 / Chapter 2.2.1 --- Self-Organizing Map in Relevance Feedback --- p.20 / Chapter 2.2.2 --- Decision Tree in Relevance Feedback --- p.22 / Chapter 2.2.3 --- Bayesian Classifier in Relevance Feedback --- p.24 / Chapter 2.2.4 --- Nearest Neighbor Search in Relevance Feedback --- p.25 / Chapter 2.2.5 --- Support Vector Machines in Relevance Feedback --- p.26 / Chapter 2.3 --- Imbalanced Classification --- p.29 / Chapter 2.4 --- Active Learning --- p.31 / Chapter 2.4.1 --- Uncertainly-based Sampling --- p.33 / Chapter 2.4.2 --- Error Reduction --- p.34 / Chapter 2.4.3 --- Batch Selection --- p.35 / Chapter 2.5 --- Convex Optimization --- p.35 / Chapter 2.5.1 --- Overview of Convex Optimization --- p.35 / Chapter 2.5.2 --- Linear Program --- p.37 / Chapter 2.5.3 --- Quadratic Program --- p.37 / Chapter 2.5.4 --- Quadratically Constrained Quadratic Program --- p.37 / Chapter 2.5.5 --- Cone Program --- p.38 / Chapter 2.5.6 --- Semi-definite Program --- p.39 / Chapter 3 --- Imbalanced Learning with BMPM for CBIR --- p.40 / Chapter 3.1 --- Research Motivation --- p.41 / Chapter 3.2 --- Background Review --- p.42 / Chapter 3.2.1 --- Relevance Feedback for CBIR --- p.42 / Chapter 3.2.2 --- Minimax Probability Machine --- p.42 / Chapter 3.2.3 --- Extensions of Minimax Probability Machine --- p.44 / Chapter 3.3 --- Relevance Feedback using BMPM --- p.45 / Chapter 3.3.1 --- Model Definition --- p.45 / Chapter 3.3.2 --- Advantages of BMPM in Relevance Feedback --- p.46 / Chapter 3.3.3 --- Relevance Feedback Framework by BMPM --- p.47 / Chapter 3.4 --- Experimental Results --- p.47 / Chapter 3.4.1 --- Experiment Datasets --- p.48 / Chapter 3.4.2 --- Performance Evaluation --- p.50 / Chapter 3.4.3 --- Discussions --- p.53 / Chapter 3.5 --- Summary --- p.53 / Chapter 4 --- BMPM Active Learning for CBIR --- p.55 / Chapter 4.1 --- Problem Statement and Motivation --- p.55 / Chapter 4.2 --- Background Review --- p.57 / Chapter 4.3 --- Relevance Feedback by BMPM Active Learning . --- p.58 / Chapter 4.3.1 --- Active Learning Concept --- p.58 / Chapter 4.3.2 --- General Approaches for Active Learning . --- p.59 / Chapter 4.3.3 --- Biased Minimax Probability Machine --- p.60 / Chapter 4.3.4 --- Proposed Framework --- p.61 / Chapter 4.4 --- Experimental Results --- p.63 / Chapter 4.4.1 --- Experiment Setup --- p.64 / Chapter 4.4.2 --- Performance Evaluation --- p.66 / Chapter 4.5 --- Summary --- p.68 / Chapter 5 --- Large Scale Learning with BMPM --- p.70 / Chapter 5.1 --- Introduction --- p.71 / Chapter 5.1.1 --- Motivation --- p.71 / Chapter 5.1.2 --- Contribution --- p.72 / Chapter 5.2 --- Background Review --- p.72 / Chapter 5.2.1 --- Second Order Cone Program --- p.72 / Chapter 5.2.2 --- General Methods for Large Scale Problems --- p.73 / Chapter 5.2.3 --- Biased Minimax Probability Machine --- p.75 / Chapter 5.3 --- Efficient BMPM Training --- p.78 / Chapter 5.3.1 --- Proposed Strategy --- p.78 / Chapter 5.3.2 --- Kernelized BMPM and Its Solution --- p.81 / Chapter 5.4 --- Experimental Results --- p.82 / Chapter 5.4.1 --- Experimental Testbeds --- p.83 / Chapter 5.4.2 --- Experimental Settings --- p.85 / Chapter 5.4.3 --- Performance Evaluation --- p.87 / Chapter 5.5 --- Summary --- p.92 / Chapter 6 --- Conclusion and Future Work --- p.93 / Chapter 6.1 --- Conclusion --- p.93 / Chapter 6.2 --- Future Work --- p.94 / Chapter A --- List of Symbols and Notations --- p.96 / Chapter B --- List of Publications --- p.98 / Bibliography --- p.100

Wong, Yuk Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 95-100). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Contribution --- p.3 / Chapter 1.3 --- Organization of This Work --- p.5 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Content-based Image Retrieval --- p.6 / Chapter 2.1.1 --- Query Technique --- p.6 / Chapter 2.1.2 --- Relevance Feedback --- p.7 / Chapter 2.1.3 --- Previously Proposed CBIR systems --- p.7 / Chapter 2.2 --- Invariant Local Feature --- p.8 / Chapter 2.3 --- Invariant Local Feature Detector --- p.9 / Chapter 2.3.1 --- Harris Corner Detector --- p.9 / Chapter 2.3.2 --- DOG Extrema Detector --- p.10 / Chapter 2.3.3 --- Harris-Laplacian Corner Detector --- p.13 / Chapter 2.3.4 --- Harris-Affine Covariant Detector --- p.14 / Chapter 2.4 --- Invariant Local Feature Descriptor --- p.15 / Chapter 2.4.1 --- Scale Invariant Feature Transform (SIFT) --- p.15 / Chapter 2.4.2 --- Shape Context --- p.17 / Chapter 2.4.3 --- PCA-SIFT --- p.18 / Chapter 2.4.4 --- Gradient Location and Orientation Histogram (GLOH) --- p.19 / Chapter 2.4.5 --- Geodesic-Intensity Histogram (GIH) --- p.19 / Chapter 2.4.6 --- Experiment --- p.21 / Chapter 2.5 --- Feature Matching --- p.27 / Chapter 2.5.1 --- Matching Criteria --- p.27 / Chapter 2.5.2 --- Distance Measures --- p.28 / Chapter 2.5.3 --- Searching Techniques --- p.29 / Chapter 3 --- A Distributed Scheme for Large-Scale CBIR --- p.31 / Chapter 3.1 --- Overview --- p.31 / Chapter 3.2 --- Related Work --- p.33 / Chapter 3.3 --- Scalable Content-Based Image Retrieval Scheme --- p.34 / Chapter 3.3.1 --- Overview of Our Solution --- p.34 / Chapter 3.3.2 --- Locality-Sensitive Hashing --- p.34 / Chapter 3.3.3 --- Scalable Indexing Solutions --- p.35 / Chapter 3.3.4 --- Disk-Based Multi-Partition Indexing --- p.36 / Chapter 3.3.5 --- Parallel Multi-Partition Indexing --- p.37 / Chapter 3.4 --- Feature Representation --- p.43 / Chapter 3.5 --- Empirical Evaluation --- p.44 / Chapter 3.5.1 --- Experimental Testbed --- p.44 / Chapter 3.5.2 --- Performance Evaluation Metrics --- p.44 / Chapter 3.5.3 --- Experimental Setup --- p.45 / Chapter 3.5.4 --- Experiment I: Disk-Based Multi-Partition Indexing Approach --- p.45 / Chapter 3.5.5 --- Experiment II: Parallel-Based Multi-Partition Indexing Approach --- p.48 / Chapter 3.6 --- Application to WWW Image Retrieval --- p.55 / Chapter 3.7 --- Summary --- p.55 / Chapter 4 --- Image Retrieval System for IND Detection --- p.60 / Chapter 4.1 --- Overview --- p.60 / Chapter 4.1.1 --- Motivation --- p.60 / Chapter 4.1.2 --- Related Work --- p.61 / Chapter 4.1.3 --- Objective --- p.62 / Chapter 4.1.4 --- Contribution --- p.63 / Chapter 4.2 --- Database Construction --- p.63 / Chapter 4.2.1 --- Image Representations --- p.63 / Chapter 4.2.2 --- Index Construction --- p.64 / Chapter 4.2.3 --- Keypoint and Image Lookup Tables --- p.67 / Chapter 4.3 --- Database Query --- p.67 / Chapter 4.3.1 --- Matching Strategies --- p.68 / Chapter 4.3.2 --- Verification Processes --- p.71 / Chapter 4.3.3 --- Image Voting --- p.75 / Chapter 4.4 --- Performance Evaluation --- p.76 / Chapter 4.4.1 --- Evaluation Metrics --- p.76 / Chapter 4.4.2 --- Results --- p.77 / Chapter 4.4.3 --- Summary --- p.81 / Chapter 5 --- Shape-SIFT Feature Descriptor --- p.82 / Chapter 5.1 --- Overview --- p.82 / Chapter 5.2 --- Related Work --- p.83 / Chapter 5.3 --- SHAPE-SIFT Descriptors --- p.84 / Chapter 5.3.1 --- Orientation assignment --- p.84 / Chapter 5.3.2 --- Canonical orientation determination --- p.84 / Chapter 5.3.3 --- Keypoint descriptor --- p.87 / Chapter 5.4 --- Performance Evaluation --- p.88 / Chapter 5.5 --- Summary --- p.90 / Chapter 6 --- Conclusions and Future Work --- p.92 / Chapter 6.1 --- Conclusions --- p.92 / Chapter 6.2 --- Future Work --- p.93 / Chapter A --- Publication --- p.94 / Bibliography --- p.95

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (p. [95]-100).

Thesis (Ph. D.)--University of North Texas, May, 2009. / Title from title page display. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (leaves 55-56).

Hoi, Chu-Hong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 89-103). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Content-based Image Retrieval --- p.1 / Chapter 1.2 --- Relevance Feedback --- p.3 / Chapter 1.3 --- Contributions --- p.4 / Chapter 1.4 --- Organization of This Work --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Relevance Feedback --- p.8 / Chapter 2.1.1 --- Heuristic Weighting Methods --- p.9 / Chapter 2.1.2 --- Optimization Formulations --- p.10 / Chapter 2.1.3 --- Various Machine Learning Techniques --- p.11 / Chapter 2.2 --- Support Vector Machines --- p.12 / Chapter 2.2.1 --- Setting of the Learning Problem --- p.12 / Chapter 2.2.2 --- Optimal Separating Hyperplane --- p.13 / Chapter 2.2.3 --- Soft-Margin Support Vector Machine --- p.15 / Chapter 2.2.4 --- One-Class Support Vector Machine --- p.16 / Chapter 3 --- Relevance Feedback with Biased SVM --- p.18 / Chapter 3.1 --- Introduction --- p.18 / Chapter 3.2 --- Biased Support Vector Machine --- p.19 / Chapter 3.3 --- Relevance Feedback Using Biased SVM --- p.22 / Chapter 3.3.1 --- Advantages of BSVM in Relevance Feedback --- p.22 / Chapter 3.3.2 --- Relevance Feedback Algorithm by BSVM --- p.23 / Chapter 3.4 --- Experiments --- p.24 / Chapter 3.4.1 --- Datasets --- p.24 / Chapter 3.4.2 --- Image Representation --- p.25 / Chapter 3.4.3 --- Experimental Results --- p.26 / Chapter 3.5 --- Discussions --- p.29 / Chapter 3.6 --- Summary --- p.30 / Chapter 4 --- Optimizing Learning with SVM Constraint --- p.31 / Chapter 4.1 --- Introduction --- p.31 / Chapter 4.2 --- Related Work and Motivation --- p.33 / Chapter 4.3 --- Optimizing Learning with SVM Constraint --- p.35 / Chapter 4.3.1 --- Problem Formulation and Notations --- p.35 / Chapter 4.3.2 --- Learning boundaries with SVM --- p.35 / Chapter 4.3.3 --- OPL for the Optimal Distance Function --- p.38 / Chapter 4.3.4 --- Overall Similarity Measure with OPL and SVM --- p.40 / Chapter 4.4 --- Experiments --- p.41 / Chapter 4.4.1 --- Datasets --- p.41 / Chapter 4.4.2 --- Image Representation --- p.42 / Chapter 4.4.3 --- Performance Evaluation --- p.43 / Chapter 4.4.4 --- Complexity and Time Cost Evaluation --- p.45 / Chapter 4.5 --- Discussions --- p.47 / Chapter 4.6 --- Summary --- p.48 / Chapter 5 --- Group-based Relevance Feedback --- p.49 / Chapter 5.1 --- Introduction --- p.49 / Chapter 5.2 --- SVM Ensembles --- p.50 / Chapter 5.3 --- Group-based Relevance Feedback Using SVM Ensembles --- p.51 / Chapter 5.3.1 --- (x+l)-class Assumption --- p.51 / Chapter 5.3.2 --- Proposed Architecture --- p.52 / Chapter 5.3.3 --- Strategy for SVM Combination and Group Ag- gregation --- p.52 / Chapter 5.4 --- Experiments --- p.54 / Chapter 5.4.1 --- Experimental Implementation --- p.54 / Chapter 5.4.2 --- Performance Evaluation --- p.55 / Chapter 5.5 --- Discussions --- p.56 / Chapter 5.6 --- Summary --- p.57 / Chapter 6 --- Log-based Relevance Feedback --- p.58 / Chapter 6.1 --- Introduction --- p.58 / Chapter 6.2 --- Related Work and Motivation --- p.60 / Chapter 6.3 --- Log-based Relevance Feedback Using SLSVM --- p.61 / Chapter 6.3.1 --- Problem Statement --- p.61 / Chapter 6.3.2 --- Soft Label Support Vector Machine --- p.62 / Chapter 6.3.3 --- LRF Algorithm by SLSVM --- p.64 / Chapter 6.4 --- Experimental Results --- p.66 / Chapter 6.4.1 --- Datasets --- p.66 / Chapter 6.4.2 --- Image Representation --- p.66 / Chapter 6.4.3 --- Experimental Setup --- p.67 / Chapter 6.4.4 --- Performance Comparison --- p.68 / Chapter 6.5 --- Discussions --- p.73 / Chapter 6.6 --- Summary --- p.75 / Chapter 7 --- Application: Web Image Learning --- p.76 / Chapter 7.1 --- Introduction --- p.76 / Chapter 7.2 --- A Learning Scheme for Searching Semantic Concepts --- p.77 / Chapter 7.2.1 --- Searching and Clustering Web Images --- p.78 / Chapter 7.2.2 --- Learning Semantic Concepts with Relevance Feed- back --- p.73 / Chapter 7.3 --- Experimental Results --- p.79 / Chapter 7.3.1 --- Dataset and Features --- p.79 / Chapter 7.3.2 --- Performance Evaluation --- p.80 / Chapter 7.4 --- Discussions --- p.82 / Chapter 7.5 --- Summary --- p.82 / Chapter 8 --- Conclusions and Future Work --- p.84 / Chapter 8.1 --- Conclusions --- p.84 / Chapter 8.2 --- Future Work --- p.85 / Chapter A --- List of Publications --- p.87 / Bibliography --- p.103

Chan Chi Hang. / Thesis submitted in: August 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 105-114). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.3 / Chapter 1.2 --- Major Contributions --- p.5 / Chapter 1.3 --- Publication List --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Background Survey --- p.9 / Chapter 2.1 --- Relevance Feedback Framework --- p.9 / Chapter 2.1.1 --- Relevance Feedback Types --- p.11 / Chapter 2.1.2 --- Data Distribution --- p.12 / Chapter 2.1.3 --- Training Set Size --- p.14 / Chapter 2.1.4 --- Inter-Query Learning and Intra-Query Learning --- p.15 / Chapter 2.2 --- History of Relevance Feedback Techniques --- p.16 / Chapter 2.3 --- Relevance Feedback Approaches --- p.19 / Chapter 2.3.1 --- Vector Space Model --- p.19 / Chapter 2.3.2 --- Ad-hoc Re-weighting --- p.26 / Chapter 2.3.3 --- Distance Optimization Approach --- p.29 / Chapter 2.3.4 --- Probabilistic Model --- p.33 / Chapter 2.3.5 --- Bayesian Approach --- p.39 / Chapter 2.3.6 --- Density Estimation Approach --- p.42 / Chapter 2.3.7 --- Support Vector Machine --- p.48 / Chapter 2.4 --- Presentation Set Selection --- p.52 / Chapter 2.4.1 --- Most-probable strategy --- p.52 / Chapter 2.4.2 --- Most-informative strategy --- p.52 / Chapter 3 --- Biased Support Vector Machine for Content-Based Image Retrieval --- p.57 / Chapter 3.1 --- Motivation --- p.57 / Chapter 3.2 --- Background --- p.58 / Chapter 3.2.1 --- Regular Support Vector Machine --- p.59 / Chapter 3.2.2 --- One-class Support Vector Machine --- p.61 / Chapter 3.3 --- Biased Support Vector Machine --- p.63 / Chapter 3.4 --- Interpretation of parameters in BSVM --- p.67 / Chapter 3.5 --- Soft Label Biased Support Vector Machine --- p.69 / Chapter 3.6 --- Interpretation of parameters in Soft Label BSVM --- p.73 / Chapter 3.7 --- Relevance Feedback Using Biased Support Vector Machine --- p.74 / Chapter 3.7.1 --- Advantages of BSVM in Relevance Feedback . . --- p.74 / Chapter 3.7.2 --- Relevance Feedback Algorithm By BSVM --- p.75 / Chapter 3.8 --- Experiments --- p.78 / Chapter 3.8.1 --- Synthetic Dataset --- p.80 / Chapter 3.8.2 --- Real-World Dataset --- p.81 / Chapter 3.8.3 --- Experimental Results --- p.83 / Chapter 3.9 --- Conclusion --- p.86 / Chapter 4 --- Self-Organizing Map-based Inter-Query Learning --- p.88 / Chapter 4.1 --- Motivation --- p.88 / Chapter 4.2 --- Algorithm --- p.89 / Chapter 4.2.1 --- Initialization and Replication of SOM --- p.89 / Chapter 4.2.2 --- SOM Training for Inter-Query Learning --- p.90 / Chapter 4.2.3 --- Incorporate with Intra-Query Learning --- p.92 / Chapter 4.3 --- Experiments --- p.93 / Chapter 4.3.1 --- Synthetic Dataset --- p.95 / Chapter 4.3.2 --- Real-World Dataset --- p.95 / Chapter 4.3.3 --- Experimental Results --- p.97 / Chapter 4.4 --- Conclusion --- p.98 / Chapter 5 --- Conclusion --- p.102 / Bibliography --- p.104

Sia Ka Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 85-91). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.1 / Chapter 1.2 --- Contributions --- p.3 / Chapter 1.3 --- Thesis Organization --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Content-Based Image Retrieval --- p.5 / Chapter 2.1.1 --- Feature Extraction --- p.6 / Chapter 2.1.2 --- Indexing and Retrieval --- p.7 / Chapter 2.2 --- Relevance Feedback --- p.7 / Chapter 2.2.1 --- Weight Updating --- p.9 / Chapter 2.2.2 --- Bayesian Formulation --- p.11 / Chapter 2.2.3 --- Statistical Approaches --- p.12 / Chapter 2.2.4 --- Inter-query Feedback --- p.12 / Chapter 2.3 --- Peer-to-Peer Information Retrieval --- p.14 / Chapter 2.3.1 --- Distributed Hash Table Techniques --- p.16 / Chapter 2.3.2 --- Routing Indices and Shortcuts --- p.17 / Chapter 2.3.3 --- Content-Based Retrieval in P2P Systems --- p.18 / Chapter 3 --- Parameter Estimation-Based Relevance Feedback --- p.21 / Chapter 3.1 --- Parameter Estimation of Target Distribution --- p.21 / Chapter 3.1.1 --- Motivation --- p.21 / Chapter 3.1.2 --- Model --- p.23 / Chapter 3.1.3 --- Relevance Feedback --- p.24 / Chapter 3.1.4 --- Maximum Entropy Display --- p.26 / Chapter 3.2 --- Self-Organizing Map Based Inter-Query Feedback --- p.27 / Chapter 3.2.1 --- Motivation --- p.27 / Chapter 3.2.2 --- Initialization and Replication of SOM --- p.29 / Chapter 3.2.3 --- SOM Training for Inter-query Feedback --- p.31 / Chapter 3.2.4 --- Target Estimation and Display Set Selection for Intra- query Feedback --- p.33 / Chapter 3.3 --- Experiment --- p.35 / Chapter 3.3.1 --- Study of Parameter Estimation Method Using Synthetic Data --- p.35 / Chapter 3.3.2 --- Performance Study in Intra- and Inter- Query Feedback . --- p.40 / Chapter 3.4 --- Conclusion --- p.42 / Chapter 4 --- Distributed COntent-based Visual Information Retrieval --- p.44 / Chapter 4.1 --- Introduction --- p.44 / Chapter 4.2 --- Peer Clustering --- p.45 / Chapter 4.2.1 --- Basic Version --- p.45 / Chapter 4.2.2 --- Single Cluster Version --- p.47 / Chapter 4.2.3 --- Multiple Clusters Version --- p.51 / Chapter 4.3 --- Firework Query Model --- p.53 / Chapter 4.4 --- Implementation and System Architecture --- p.57 / Chapter 4.4.1 --- Gnutella Message Modification --- p.57 / Chapter 4.4.2 --- Architecture of DISCOVIR --- p.59 / Chapter 4.4.3 --- Flow of Operations --- p.60 / Chapter 4.5 --- Experiments --- p.62 / Chapter 4.5.1 --- Simulation Model of the Peer-to-Peer Network --- p.62 / Chapter 4.5.2 --- Number of Peers --- p.66 / Chapter 4.5.3 --- TTL of Query Message --- p.70 / Chapter 4.5.4 --- Effects of Data Resolution on Query Efficiency --- p.73 / Chapter 4.5.5 --- Discussion --- p.74 / Chapter 4.6 --- Conclusion --- p.77 / Chapter 5 --- Future Works and Conclusion --- p.79 / Chapter A --- Derivation of Update Equation --- p.81 / Chapter B --- An Efficient Discovery of Signatures --- p.82 / Bibliography --- p.85