by Stephen Chi-tin Yang. / "Sept. 28, 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 158-163). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Tsik Chung Wai Benjamin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 67-70). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Automatic Speech Recognition --- p.1 / Chapter 1.2 --- Cantonese Speech Recognition --- p.3 / Chapter 1.3 --- Neural Networks --- p.4 / Chapter 1.4 --- About this Thesis --- p.5 / Chapter 2 --- The Phonology of Cantonese --- p.6 / Chapter 2.1 --- The Syllabic Structure of Cantonese Syllable --- p.7 / Chapter 2.2 --- The Tone System of Cantonese --- p.9 / Chapter 3 --- Review of Automatic Speech Recognition Systems --- p.12 / Chapter 3.1 --- Hidden Markov Model Approach --- p.12 / Chapter 3.2 --- Neural Networks Approach --- p.13 / Chapter 3.2.1 --- Multi-Layer Perceptrons (MLP) --- p.13 / Chapter 3.2.2 --- Time-Delay Neural Networks (TDNN) --- p.15 / Chapter 3.2.3 --- Recurrent Neural Networks --- p.17 / Chapter 3.3 --- Integrated Approach --- p.18 / Chapter 3.4 --- Mandarin and Cantonese Speech Recognition Systems --- p.19 / Chapter 4 --- The Speech Corpus and Database --- p.21 / Chapter 4.1 --- Design of the Speech Corpus --- p.21 / Chapter 4.2 --- Speech Database Acquisition --- p.23 / Chapter 5 --- Feature Parameters Extraction --- p.24 / Chapter 5.1 --- Endpoint Detection --- p.25 / Chapter 5.2 --- Speech Processing --- p.26 / Chapter 5.3 --- Speech Segmentation --- p.27 / Chapter 5.4 --- Phoneme Feature Extraction --- p.29 / Chapter 5.5 --- Tone Feature Extraction --- p.30 / Chapter 6 --- The Design of the System --- p.33 / Chapter 6.1 --- Towards Large Vocabulary System --- p.34 / Chapter 6.2 --- Overview of the Isolated Cantonese Syllable Recognition System --- p.36 / Chapter 6.3 --- The Primary Level: Phoneme Classifiers and Tone Classifier --- p.38 / Chapter 6.4 --- The Intermediate Level: Ending Corrector --- p.42 / Chapter 6.5 --- The Secondary Level: Syllable Classifier --- p.43 / Chapter 6.5.1 --- Concatenation with Correction Approach --- p.44 / Chapter 6.5.2 --- Fuzzy ART Approach --- p.45 / Chapter 7 --- Computer Simulation --- p.49 / Chapter 7.1 --- Experimental Conditions --- p.49 / Chapter 7.2 --- Experimental Results of the Primary Level Classifiers --- p.50 / Chapter 7.3 --- Overall Performance of the System --- p.57 / Chapter 7.4 --- Discussions --- p.61 / Chapter 8 --- Further Works --- p.62 / Chapter 8.1 --- Enhancement on Speech Segmentation --- p.62 / Chapter 8.2 --- Towards Speaker-Independent System --- p.63 / Chapter 8.3 --- Towards Speech-to-Text System --- p.64 / Chapter 9 --- Conclusions --- p.65 / Bibliography --- p.67 / Appendix A. Cantonese Syllable Full Set List --- p.71

by Evan, Fung-yu Young. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 161-166). / List of Figures --- p.vi / List of Tables --- p.vii / List of Graphs --- p.viii / Abstract --- p.ix / Chapter Part I --- Learning Algorithms / Chapter 1 --- Representing Time in Connectionist Models --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Temporal Sequences --- p.2 / Chapter 1.2.1 --- Recognition Tasks --- p.2 / Chapter 1.2.2 --- Reproduction Tasks --- p.3 / Chapter 1.2.3 --- Generation Tasks --- p.4 / Chapter 1.3 --- Discrete Time v.s. Continuous Time --- p.4 / Chapter 1.4 --- Time Delay Neural Network (TDNN) --- p.4 / Chapter 1.4.1 --- Delay Elements in the Connections --- p.5 / Chapter 1.4.2 --- NETtalk: An Application of TDNN --- p.7 / Chapter 1.4.3 --- Drawbacks of TDNN --- p.8 / Chapter 1.5 --- Networks with Context Units --- p.8 / Chapter 1.5.1 --- Jordan's Network --- p.9 / Chapter 1.5.2 --- Elman's Network --- p.10 / Chapter 1.5.3 --- Other Architectures --- p.14 / Chapter 1.5.4 --- Drawbacks of Using Context Units --- p.15 / Chapter 1.6 --- Recurrent Neural Networks --- p.16 / Chapter 1.6.1 --- Hopfield Models --- p.17 / Chapter 1.6.2 --- Fully Recurrent Neural Networks --- p.20 / Chapter A. --- EXAMPLES OF USING RECURRENT NETWORKS --- p.22 / Chapter 1.7 --- Our Objective --- p.25 / Chapter 2 --- Learning Algorithms for Recurrent Neural Networks --- p.27 / Chapter 2.1 --- Introduction --- p.27 / Chapter 2.2 --- Gradient Descent Methods --- p.29 / Chapter 2.2.1 --- Backpropagation Through Time (BPTT) --- p.29 / Chapter 2.2.2 --- Real Time Recurrent Learning Rule (RTRL) --- p.30 / Chapter A. --- RTRL WITH TEACHER FORCING --- p.32 / Chapter B. --- TERMINAL TEACHER FORCING --- p.33 / Chapter C. --- CONTINUOUS TIME RTRL --- p.33 / Chapter 2.2.3 --- Variants of RTRL --- p.34 / Chapter A. --- SUB GROUPED RTRL --- p.34 / Chapter B. --- A FIXED SIZE STORAGE 0(n3) TIME COMPLEXITY LEARNGING RULE --- p.35 / Chapter 2.3 --- Non-Gradient Descent Methods --- p.37 / Chapter 2.3.1 --- Neural Bucket Brigade (NBB) --- p.37 / Chapter 2.3.2 --- Temporal Driven Method (TO) --- p.38 / Chapter 2.4 --- Comparison between Different Approaches --- p.39 / Chapter 2.5 --- Conclusion --- p.41 / Chapter 3 --- Locally Connected Recurrent Networks --- p.43 / Chapter 3.1 --- Introduction --- p.43 / Chapter 3.2 --- Locally Connected Recurrent Networks --- p.44 / Chapter 3.2.1 --- Network Topology --- p.44 / Chapter 3.2.2 --- Subgrouping --- p.46 / Chapter 3.2.3 --- Learning Algorithm --- p.47 / Chapter 3.2.4 --- Continuous Time Learning Algorithm --- p.50 / Chapter 3.3 --- Analysis --- p.51 / Chapter 3.3.1 --- Time Complexity --- p.51 / Chapter 3.3.2 --- Space Complexity --- p.51 / Chapter 3.3.3 --- Local Computations in Time and Space --- p.51 / Chapter 3.4 --- Running on Parallel Architectures --- p.52 / Chapter 3.4.1 --- Mapping the Algorithm to Parallel Architectures --- p.52 / Chapter 3.4.2 --- Parallel Learning Algorithm --- p.53 / Chapter 3.4.3 --- Analysis --- p.54 / Chapter 3.5 --- Ring-Structured Recurrent Network (RRN) --- p.55 / Chapter 3.6 --- Comparison between RRN and RTRL in Sequence Recognition --- p.55 / Chapter 3.6.1 --- Training Sets and Testing Sequences --- p.56 / Chapter 3.6.2 --- Comparison in Training Speed --- p.58 / Chapter 3.6.3 --- Comparison in Recalling Power --- p.59 / Chapter 3.7 --- Comparison between RRN and RTRL in Time Series Prediction --- p.59 / Chapter 3.7.1 --- Comparison in Training Speed --- p.62 / Chapter 3.7.2 --- Comparison in Predictive Power --- p.63 / Chapter 3.8 --- Conclusion --- p.65 / Chapter Part II --- Applications / Chapter 4 --- Sequence Recognition by Ring-Structured Recurrent Networks --- p.67 / Chapter 4.1 --- Introduction --- p.67 / Chapter 4.2 --- Related Works --- p.68 / Chapter 4.2.1 --- Feedback Multilayer Perceptron (FMLP) --- p.68 / Chapter 4.2.2 --- Back Propagation Unfolded Recurrent Rule (BURR) --- p.69 / Chapter 4.3 --- Experimental Details --- p.71 / Chapter 4.3.1 --- Network Architecture --- p.71 / Chapter 4.3.2 --- Input/Output Representations --- p.72 / Chapter 4.3.3 --- Training Phase --- p.73 / Chapter 4.3.4 --- Recalling Phase --- p.73 / Chapter 4.4 --- Experimental Results --- p.74 / Chapter 4.4.1 --- Temporal Memorizing Power --- p.74 / Chapter 4.4.2 --- Time Warping Performance --- p.80 / Chapter 4.4.3 --- Fault Tolerance --- p.85 / Chapter 4.4.4 --- Learning Rate --- p.87 / Chapter 4.5 --- Time Delay --- p.88 / Chapter 4.6 --- Conclusion --- p.91 / Chapter 5 --- Time Series Prediction --- p.92 / Chapter 5.1 --- Introduction --- p.92 / Chapter 5.2 --- Modelling in Feedforward Networks --- p.93 / Chapter 5.3 --- Methodology with Recurrent Networks --- p.94 / Chapter 5.3.1 --- Network Structure --- p.94 / Chapter 5.3.2 --- Model Building - Training --- p.95 / Chapter 5.3.3 --- Model Diagnosis - Testing --- p.95 / Chapter 5.4 --- Training Paradigms --- p.96 / Chapter 5.4.1 --- A Quasiperiodic Series with White Noise --- p.96 / Chapter 5.4.2 --- A Chaotic Series --- p.97 / Chapter 5.4.3 --- Sunspots Numbers --- p.98 / Chapter 5.4.4 --- Hang Seng Index --- p.99 / Chapter 5.5 --- Experimental Results and Discussions --- p.99 / Chapter 5.5.1 --- A Quasiperiodic Series with White Noise --- p.101 / Chapter 5.5.2 --- Logistic Map --- p.103 / Chapter 5.5.3 --- Sunspots Numbers --- p.105 / Chapter 5.5.4 --- Hang Seng Index --- p.109 / Chapter 5.6 --- Conclusion --- p.112 / Chapter 6 --- Chaos in Recurrent Networks --- p.114 / Chapter 6.1 --- Introduction --- p.114 / Chapter 6.2 --- Important Features of Chaos --- p.115 / Chapter 6.2.1 --- First Return Map --- p.115 / Chapter 6.2.2 --- Long Term Unpredictability --- p.117 / Chapter 6.2.3 --- Sensitivity to Initial Conditions (SIC) --- p.118 / Chapter 6.2.4 --- Strange Attractor --- p.119 / Chapter 6.3 --- Chaotic Behaviour in Recurrent Networks --- p.120 / Chapter 6.3.1 --- Network Structure --- p.121 / Chapter 6.3.2 --- Dynamics in Training --- p.121 / Chapter 6.3.3 --- Dynamics in Testing --- p.122 / Chapter 6.4 --- Experiments and Discussions --- p.123 / Chapter 6.4.1 --- Henon Model --- p.123 / Chapter 6.4.2 --- Lorenz Model --- p.127 / Chapter 6.5 --- Conclusion --- p.134 / Chapter 7 --- Conclusion --- p.135 / Appendix A Series 1 Sine Function with White Noise --- p.137 / Appendix B Series 2 Logistic Map --- p.138 / Appendix C Series 3 Sunspots Numbers from 1700 to 1979 --- p.139 / Appendix D A Quasiperiodic Series with White Noise --- p.141 / Appendix E Hang Seng Daily Closing Index in 1991 --- p.142 / Appendix F Network Model for the Quasiperiodic Series with White Noise --- p.143 / Appendix G Network Model for the Logistic Map --- p.144 / Appendix H Network Model for the Sunspots Numbers --- p.145 / Appendix I Network Model for the Hang Seng Index --- p.146 / Appendix J Henon Model --- p.147 / Appendix K Network Model for the Henon Map --- p.150 / Appendix L Lorenz Model --- p.151 / Appendix M Network Model for the Lorenz Map --- p.159 / Bibliography --- p.161

by Hau-san Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [178-183]). / Chapter 1 --- INTRODUCTION / Chapter 1.1 --- Learning versus Explicit Programming --- p.1-1 / Chapter 1.2 --- Artificial Neural Networks --- p.1-2 / Chapter 1.3 --- Learning in ANN --- p.1-3 / Chapter 1.4 --- Problems of Learning in BP Networks --- p.1-5 / Chapter 1.5 --- Dynamic Node Architecture for BP Networks --- p.1-7 / Chapter 1.6 --- Incremental Learning --- p.1-10 / Chapter 1.7 --- Research Objective and Thesis Organization --- p.1-11 / Chapter 2 --- THE FEEDFORWARD MULTILAYER NEURAL NETWORK / Chapter 2.1 --- The Perceptron --- p.2-1 / Chapter 2.2 --- The Generalization of the Perceptron --- p.2-4 / Chapter 2.3 --- The Multilayer Feedforward Network --- p.2-5 / Chapter 3 --- SOLUTIONS TO THE BP LEARNING PROBLEM / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- Attempts in the Establishment of a Viable Hidden Representation Model --- p.3-5 / Chapter 3.3 --- Dynamic Node Creation Algorithms --- p.3-9 / Chapter 3.4 --- Concluding Remarks --- p.3-15 / Chapter 4 --- THE GROWTH ALGORITHM FOR NEURAL NETWORKS / Chapter 4.1 --- Introduction --- p.4-2 / Chapter 4.2 --- The Radial Basis Function --- p.4-6 / Chapter 4.3 --- The Additional Input Node and the Modified Nonlinearity --- p.4-9 / Chapter 4.4 --- The Initialization of the New Hidden Node --- p.4-11 / Chapter 4.5 --- Initialization of the First Node --- p.4-15 / Chapter 4.6 --- Practical Considerations for the Growth Algorithm --- p.4-18 / Chapter 4.7 --- The Convergence Proof for the Growth Algorithm --- p.4-20 / Chapter 4.8 --- The Flow of the Growth Algorithm --- p.4-21 / Chapter 4.9 --- Experimental Results and Performance Analysis --- p.4-21 / Chapter 4.10 --- Concluding Remarks --- p.4-33 / Chapter 5 --- KNOWLEDGE REPRESENTATION IN NEURAL NETWORKS / Chapter 5.1 --- An Alternative Perspective to Knowledge Representation in Neural Network: The Temporal Vector (T-Vector) Approach --- p.5-1 / Chapter 5.2 --- Prior Research Works in the T-Vector Approach --- p.5-2 / Chapter 5.3 --- Formulation of the T-Vector Approach --- p.5-3 / Chapter 5.4 --- Relation of the Hidden T-Vectors to the Output T-Vectors --- p.5-6 / Chapter 5.5 --- Relation of the Hidden T-Vectors to the Input T-Vectors --- p.5-10 / Chapter 5.6 --- An Inspiration for a New Training Algorithm from the Current Model --- p.5-12 / Chapter 6 --- THE DETERMINISTIC TRAINING ALGORITHM FOR NEURAL NETWORKS / Chapter 6.1 --- Introduction --- p.6-1 / Chapter 6.2 --- The Linear Independency Requirement for the Hidden T-Vectors --- p.6-3 / Chapter 6.3 --- Inspiration of the Current Work from the Barmann T-Vector Model --- p.6-5 / Chapter 6.4 --- General Framework of Dynamic Node Creation Algorithm --- p.6-10 / Chapter 6.5 --- The Deterministic Initialization Scheme for the New Hidden Nodes / Chapter 6.5.1 --- Introduction --- p.6-12 / Chapter 6.5.2 --- Determination of the Target T-Vector / Chapter 6.5.2.1 --- Introduction --- p.6-15 / Chapter 6.5.2.2 --- Modelling of the Target Vector βQhQ --- p.6-16 / Chapter 6.5.2.3 --- Near-Linearity Condition for the Sigmoid Function --- p.6-18 / Chapter 6.5.3 --- Preparation for the BP Fine-Tuning Process --- p.6-24 / Chapter 6.5.4 --- Determination of the Target Hidden T-Vector --- p.6-28 / Chapter 6.5.5 --- Determination of the Hidden Weights --- p.6-29 / Chapter 6.5.6 --- Determination of the Output Weights --- p.6-30 / Chapter 6.6 --- Linear Independency Assurance for the New Hidden T-Vector --- p.6-30 / Chapter 6.7 --- Extension to the Multi-Output Case --- p.6-32 / Chapter 6.8 --- Convergence Proof for the Deterministic Algorithm --- p.6-35 / Chapter 6.9 --- The Flow of the Deterministic Dynamic Node Creation Algorithm --- p.6-36 / Chapter 6.10 --- Experimental Results and Performance Analysis --- p.6-36 / Chapter 6.11 --- Concluding Remarks --- p.6-50 / Chapter 7 --- THE GENERALIZATION MEASURE MONITORING SCHEME / Chapter 7.1 --- The Problem of Generalization for Neural Networks --- p.7-1 / Chapter 7.2 --- Prior Attempts in Solving the Generalization Problem --- p.7-2 / Chapter 7.3 --- The Generalization Measure --- p.7-4 / Chapter 7.4 --- The Adoption of the Generalization Measure to the Deterministic Algorithm --- p.7-5 / Chapter 7.5 --- Monitoring of the Generalization Measure --- p.7-6 / Chapter 7.6 --- Correspondence between the Generalization Measure and the Generalization Capability of the Network --- p.7-8 / Chapter 7.7 --- Experimental Results and Performance Analysis --- p.7-12 / Chapter 7.8 --- Concluding Remarks --- p.7-16 / Chapter 8 --- THE ESTIMATION OF THE INITIAL HIDDEN LAYER SIZE / Chapter 8.1 --- The Need for an Initial Hidden Layer Size Estimation --- p.8-1 / Chapter 8.2 --- The Initial Hidden Layer Estimation Scheme --- p.8-2 / Chapter 8.3 --- The Extension of the Estimation Procedure to the Multi-Output Network --- p.8-6 / Chapter 8.4 --- Experimental Results and Performance Analysis --- p.8-6 / Chapter 8.5 --- Concluding Remarks --- p.8-16 / Chapter 9 --- CONCLUSION / Chapter 9.1 --- Contributions --- p.9-1 / Chapter 9.2 --- Suggestions for Further Research --- p.9-3 / REFERENCES --- p.R-1 / APPENDIX --- p.A-1

by Vincent Wai-leuk Tam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 74-80). / Chapter 1 --- Introduction and Summary --- p.1 / Chapter 1.1 --- The Task --- p.1 / Chapter 1.2 --- The Thesis --- p.2 / Chapter 1.2.1 --- Thesis --- p.2 / Chapter 1.2.2 --- Antithesis --- p.3 / Chapter 1.2.3 --- Synthesis --- p.5 / Chapter 1.3 --- Results --- p.6 / Chapter 1.4 --- Contributions --- p.6 / Chapter 1.5 --- Chapter Summaries --- p.7 / Chapter 1.5.1 --- Chapter 2: An ANN-Based Constraint-Solver --- p.8 / Chapter 1.5.2 --- Chapter 3: A Theoretical Framework of PROCLANN --- p.8 / Chapter 1.5.3 --- Chapter 4: The Prototype Implementation --- p.8 / Chapter 1.5.4 --- Chapter 5: Benchmarking --- p.9 / Chapter 1.5.5 --- Chapter 6: Conclusion --- p.9 / Chapter 2 --- An ANN-Based Constraint-Solver --- p.10 / Chapter 2.1 --- Notations --- p.11 / Chapter 2.2 --- Criteria for ANN-based Constraint-solver --- p.11 / Chapter 2.3 --- A Generic Neural Network: GENET --- p.13 / Chapter 2.3.1 --- Network Structure --- p.13 / Chapter 2.3.2 --- Network Convergence --- p.17 / Chapter 2.3.3 --- Energy Perspective --- p.22 / Chapter 2.4 --- Properties of GENET --- p.23 / Chapter 2.5 --- Incremental GENET --- p.27 / Chapter 3 --- A Theoretical Framework of PROCLANN --- p.29 / Chapter 3.1 --- Syntax and Declarative Semantics --- p.30 / Chapter 3.2 --- Unification in PROCLANN --- p.33 / Chapter 3.3 --- PROCLANN Computation Model --- p.38 / Chapter 3.4 --- Soundness and Weak Completeness of the PROCLANN Compu- tation Model --- p.40 / Chapter 3.5 --- Probabilistic Non-determinism --- p.46 / Chapter 4 --- The Prototype Implementation --- p.48 / Chapter 4.1 --- Prototype Design --- p.48 / Chapter 4.2 --- Implementation Issues --- p.52 / Chapter 5 --- Benchmarking --- p.58 / Chapter 5.1 --- N-Queens --- p.59 / Chapter 5.1.1 --- Benchmarking --- p.59 / Chapter 5.1.2 --- Analysis --- p.59 / Chapter 5.2 --- Graph-coloring --- p.63 / Chapter 5.2.1 --- Benchmarking --- p.63 / Chapter 5.2.2 --- Analysis --- p.64 / Chapter 5.3 --- Exceptionally Hard Problem --- p.66 / Chapter 5.3.1 --- Benchmarking --- p.67 / Chapter 5.3.2 --- Analysis --- p.67 / Chapter 6 --- Conclusion --- p.68 / Chapter 6.1 --- Contributions --- p.68 / Chapter 6.2 --- Limitations --- p.70 / Chapter 6.3 --- Future Work --- p.71 / Chapter 6.3.1 --- Parallel Implementation --- p.71 / Chapter 6.3.2 --- General Constraint Handling --- p.72 / Chapter 6.3.3 --- Other ANN Models --- p.73 / Chapter 6.3.4 --- Other Domains --- p.73 / Bibliography --- p.74 / Appendix A The Hard Graph-coloring Problems --- p.81 / Appendix B An Exceptionally Hard Problem (EHP) --- p.182

by Yip Pak Ching. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 97-100). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- The Objective of Machine Vision --- p.3 / Chapter 1.3 --- Our Goal --- p.4 / Chapter 1.4 --- Previous Works and Current Research --- p.5 / Chapter 1.5 --- Organization of the Thesis --- p.8 / Chapter 2 --- Human Movement Perception --- p.11 / Chapter 2.1 --- Basic Mechanisms of Vision --- p.11 / Chapter 2.2 --- Functions of Movement Perception --- p.12 / Chapter 2.3 --- Five Ways to Make a Spot of Light Appear to Move --- p.14 / Chapter 2.4 --- Real Movement --- p.15 / Chapter 2.5 --- Mechanisms for the Perception of Real Movement --- p.16 / Chapter 2.6 --- Apparent Motion --- p.18 / Chapter 3 --- Machine Movement Perception --- p.21 / Chapter 3.1 --- Introduction --- p.21 / Chapter 3.2 --- Perspective Transformation --- p.21 / Chapter 3.3 --- Motion Detection by Difference Image --- p.22 / Chapter 3.4 --- Accumulative Difference --- p.24 / Chapter 3.5 --- Establishing a Reference Image --- p.26 / Chapter 3.6 --- Optical Flow --- p.27 / Chapter 4 --- Neural Networks for Machine Vision --- p.30 / Chapter 4.1 --- Introduction --- p.30 / Chapter 4.2 --- Perceptron --- p.30 / Chapter 4.3 --- The Back-Propagation Training Algorithm --- p.33 / Chapter 4.4 --- Object Identification --- p.34 / Chapter 4.5 --- Special Technique for Improving the Learning Time and Recognition Rate --- p.36 / Chapter 5 --- Neural Networks by Supervised Learning for Motion Detection --- p.39 / Chapter 5.1 --- Introduction --- p.39 / Chapter 5.2 --- Three-Level Network Architecture --- p.40 / Chapter 5.3 --- Four-Level Network Architecture --- p.45 / Chapter 6 --- Rough Motion Detection --- p.50 / Chapter 6.1 --- Introduction --- p.50 / Chapter 6.2 --- The Rough Motion Detection Network --- p.51 / Chapter 6.3 --- The Correlation Network --- p.54 / Chapter 6.4 --- Modified Rough Motion Detection Network --- p.56 / Chapter 7 --- Moving Object Extraction --- p.59 / Chapter 7.1 --- Introduction --- p.59 / Chapter 7.2 --- Three Types of Images for Moving Object Extraction --- p.59 / Chapter 7.3 --- Edge Enhancement Network --- p.62 / Chapter 7.4 --- Background Remover --- p.63 / Chapter 8 --- Motion Parameter Extraction --- p.66 / Chapter 8.1 --- Introduction --- p.66 / Chapter 8.2 --- 2-D Motion Detection --- p.66 / Chapter 8.3 --- Normalization Network --- p.67 / Chapter 8.4 --- 3-D Motion Parameter Extraction --- p.70 / Chapter 8.5 --- Object Identification --- p.70 / Chapter 9 --- Motion Parameter Extraction from Overlapped Object Images --- p.72 / Chapter 9.1 --- Introduction --- p.72 / Chapter 9.2 --- Decision Network --- p.72 / Chapter 9.3 --- Motion Direction Extraction from Overlapped Object Images by Three-Level Network Model with Supervised Learning --- p.75 / Chapter 9.4 --- Readjustment Network for Motion Parameter Extraction from Overlapped Object Images --- p.79 / Chapter 9.5 --- Reconstruction of the Overlapped object Image --- p.82 / Chapter 10 --- The Integrated Motion Detection System --- p.87 / Chapter 10.1 --- Introduction --- p.87 / Chapter 10.2 --- System Architecture --- p.88 / Chapter 10.3 --- Results and Concluding Remarks --- p.91 / Chapter 11 --- Conclusion --- p.93 / References --- p.97

by Tan Lee. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references. / by Tan Lee. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Conventional Pattern Recognition Approaches for Speech Recognition --- p.3 / Chapter 1.2 --- A Review on Neural Network Applications in Speech Recognition --- p.6 / Chapter 1.2.1 --- Static Pattern Classification --- p.7 / Chapter 1.2.2 --- Hybrid Approaches --- p.9 / Chapter 1.2.3 --- Dynamic Neural Networks --- p.12 / Chapter 1.3 --- Automatic Recognition of Cantonese Speech --- p.16 / Chapter 1.4 --- Organization of the Thesis --- p.18 / References --- p.20 / Chapter 2 --- Phonological and Acoustical Properties of Cantonese Syllables --- p.29 / Chapter 2.1 --- Phonology of Cantonese --- p.29 / Chapter 2.1.1 --- Basic Phonetic Units --- p.30 / Chapter 2.1.2 --- Syllabic Structure --- p.32 / Chapter 2.1.3 --- Lexical Tones --- p.33 / Chapter 2.2 --- Acoustical Properties of Cantonese Syllables --- p.35 / Chapter 2.2.1 --- Spectral Features --- p.35 / Chapter 2.2.2 --- Energy and Zero-Crossing Rate --- p.39 / Chapter 2.2.3 --- Pitch --- p.40 / Chapter 2.2.4 --- Duration --- p.41 / Chapter 2.3 --- Acoustic Feature Extraction for Speech Recognition of Cantonese --- p.42 / References --- p.46 / Chapter 3 --- Tone Recognition of Isolated Cantonese Syllables --- p.48 / Chapter 3.1 --- Acoustic Pre-processing --- p.48 / Chapter 3.1.1 --- Voiced Portion Detection --- p.48 / Chapter 3.1.2 --- Pitch Extraction --- p.51 / Chapter 3.2 --- Supra-Segmental Feature Parameters for Tone Recognition --- p.53 / Chapter 3.2.1 --- Pitch-Related Feature Parameters --- p.53 / Chapter 3.2.2 --- Duration and Energy Drop Rate --- p.55 / Chapter 3.2.3 --- Normalization of Feature Parameters --- p.57 / Chapter 3.3 --- An MLP Based Tone Classifier --- p.58 / Chapter 3.4 --- Simulation Experiments --- p.59 / Chapter 3.4.1 --- Speech Data --- p.59 / Chapter 3.4.2 --- Feature Extraction and Normalization --- p.61 / Chapter 3.4.3 --- Experimental Results --- p.61 / Chapter 3.5 --- Discussion and Conclusion --- p.64 / References --- p.65 / Chapter 4 --- Recurrent Neural Network Based Dynamic Speech Models --- p.67 / Chapter 4.1 --- Motivations and Rationales --- p.68 / Chapter 4.2 --- RNN Speech Model (RSM) --- p.71 / Chapter 4.2.1 --- Network Architecture and Dynamic Operation --- p.71 / Chapter 4.2.2 --- RNN for Speech Modeling --- p.72 / Chapter 4.2.3 --- Illustrative Examples --- p.75 / Chapter 4.3 --- Training of RNN Speech Models --- p.78 / Chapter 4.3.1 --- Real-Time-Recurrent-Learning (RTRL) Algorithm --- p.78 / Chapter 4.3.2 --- Iterative Re-segmentation Training of RSM --- p.80 / Chapter 4.4 --- Several Practical Issues in RSM Training --- p.85 / Chapter 4.4.1 --- Combining Adjacent Segments --- p.85 / Chapter 4.4.2 --- Hypothesizing Initial Segmentation --- p.86 / Chapter 4.4.3 --- Improving Temporal State Dependency --- p.89 / Chapter 4.5 --- Simulation Experiments --- p.90 / Chapter 4.5.1 --- Experiment 4.1 - Training with a Single Utterance --- p.91 / Chapter 4.5.2 --- Experiment 4.2 - Effect of Augmenting Recurrent Learning Rate --- p.93 / Chapter 4.5.3 --- Experiment 4.3 - Training with Multiple Utterances --- p.96 / Chapter 4.5.4 --- Experiment 4.4 一 Modeling Performance of RSMs --- p.99 / Chapter 4.6 --- Conclusion --- p.104 / References --- p.106 / Chapter 5 --- Isolated Word Recognition Using RNN Speech Models --- p.107 / Chapter 5.1 --- A Baseline System --- p.107 / Chapter 5.1.1 --- System Description --- p.107 / Chapter 5.1.2 --- Simulation Experiments --- p.110 / Chapter 5.1.3 --- Discussion --- p.117 / Chapter 5.2 --- Incorporating Duration Information --- p.118 / Chapter 5.2.1 --- Duration Screening --- p.118 / Chapter 5.2.2 --- Determination of Duration Bounds --- p.120 / Chapter 5.2.3 --- Simulation Experiments --- p.120 / Chapter 5.2.4 --- Discussion --- p.124 / Chapter 5.3 --- Discriminative Training --- p.125 / Chapter 5.3.1 --- The Minimum Classification Error Formulation --- p.126 / Chapter 5.3.2 --- Generalized Probabilistic Descent Algorithm --- p.127 / Chapter 5.3.3 --- Determination of Training Parameters --- p.128 / Chapter 5.3.4 --- Simulation Experiments --- p.129 / Chapter 5.3.5 --- Discussion --- p.133 / Chapter 5.4 --- Conclusion --- p.134 / References --- p.135 / Chapter 6 --- An Integrated Speech Recognition System for Cantonese Syllables --- p.137 / Chapter 6.1 --- System Architecture and Recognition Scheme --- p.137 / Chapter 6.2 --- Speech Corpus and Data Pre-processing --- p.140 / Chapter 6.3 --- Recognition Experiments and Results --- p.140 / Chapter 6.4 --- Discussion and Conclusion --- p.144 / References --- p.146 / Chapter 7 --- Conclusions and Suggestions for Future Work --- p.147 / Chapter 7.1 --- Conclusions --- p.147 / Chapter 7.2 --- Suggestions for Future Work --- p.151

by H.S. Ng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 91-97). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Neuro-connectionist Network --- p.2 / Chapter 2 --- Related Works --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.1.1 --- Kruskal's Algorithm --- p.5 / Chapter 2.1.2 --- Prim's algorithm --- p.6 / Chapter 2.1.3 --- Sollin's algorithm --- p.7 / Chapter 2.1.4 --- Bellman-Ford algorithm --- p.8 / Chapter 2.1.5 --- Floyd-Warshall algorithm --- p.9 / Chapter 3 --- Binary Relation Inference Network and Path Problems --- p.11 / Chapter 3.1 --- Introduction --- p.11 / Chapter 3.2 --- Topology --- p.12 / Chapter 3.3 --- Network structure --- p.13 / Chapter 3.3.1 --- Single-destination BRIN architecture --- p.14 / Chapter 3.3.2 --- Comparison between all-pair BRIN and single-destination BRIN --- p.18 / Chapter 3.4 --- Path Problems and BRIN Solution --- p.18 / Chapter 3.4.1 --- Minimax path problems --- p.18 / Chapter 3.4.2 --- BRIN solution --- p.19 / Chapter 4 --- Analog and Voltage-mode Approach --- p.22 / Chapter 4.1 --- Introduction --- p.22 / Chapter 4.2 --- Analog implementation --- p.24 / Chapter 4.3 --- Voltage-mode approach --- p.26 / Chapter 4.3.1 --- The site function --- p.26 / Chapter 4.3.2 --- The unit function --- p.28 / Chapter 4.3.3 --- The computational unit --- p.28 / Chapter 4.4 --- Conclusion --- p.29 / Chapter 5 --- Current-mode Approach --- p.32 / Chapter 5.1 --- Introduction --- p.32 / Chapter 5.2 --- Current-mode approach for analog VLSI Implementation --- p.33 / Chapter 5.2.1 --- Site and Unit output function --- p.33 / Chapter 5.2.2 --- Computational unit --- p.34 / Chapter 5.2.3 --- A complete network --- p.35 / Chapter 5.3 --- Conclusion --- p.37 / Chapter 6 --- Neural Network Compensation for Optimization Circuit --- p.40 / Chapter 6.1 --- Introduction --- p.40 / Chapter 6.2 --- A Neuro-connectionist Architecture for error correction --- p.41 / Chapter 6.2.1 --- Linear Relationship --- p.42 / Chapter 6.2.2 --- Output Deviation of Computational Unit --- p.44 / Chapter 6.3 --- Experimental Results --- p.46 / Chapter 6.3.1 --- Training Phase --- p.46 / Chapter 6.3.2 --- Generalization Phase --- p.48 / Chapter 6.4 --- Conclusion --- p.50 / Chapter 7 --- Precision-limited Analog Neural Network Compensation --- p.51 / Chapter 7.1 --- Introduction --- p.51 / Chapter 7.2 --- Analog Neural Network hardware --- p.53 / Chapter 7.3 --- Integration of analog neural network compensation of connectionist net- work for general path problems --- p.54 / Chapter 7.4 --- Experimental Results --- p.55 / Chapter 7.4.1 --- Convergence time --- p.56 / Chapter 7.4.2 --- The accuracy of the system --- p.57 / Chapter 7.5 --- Conclusion --- p.58 / Chapter 8 --- Transitive Closure Problems --- p.60 / Chapter 8.1 --- Introduction --- p.60 / Chapter 8.2 --- Different ways of implementation of BRIN for transitive closure --- p.61 / Chapter 8.2.1 --- Digital Implementation --- p.61 / Chapter 8.2.2 --- Analog Implementation --- p.61 / Chapter 8.3 --- Transitive Closure Problem --- p.63 / Chapter 8.3.1 --- A special case of maximum spanning tree problem --- p.64 / Chapter 8.3.2 --- Analog approach solution for transitive closure problem --- p.65 / Chapter 8.3.3 --- Current-mode approach solution for transitive closure problem --- p.67 / Chapter 8.4 --- Comparisons between the different forms of implementation of BRIN for transitive closure --- p.71 / Chapter 8.4.1 --- Convergence Time --- p.71 / Chapter 8.4.2 --- Circuit complexity --- p.72 / Chapter 8.5 --- Discussion --- p.73 / Chapter 9 --- Critical path problems --- p.74 / Chapter 9.1 --- Introduction --- p.74 / Chapter 9.2 --- Problem statement and single-destination BRIN solution --- p.75 / Chapter 9.3 --- Analog implementation --- p.76 / Chapter 9.3.1 --- Separated building block --- p.78 / Chapter 9.3.2 --- Combined building block --- p.79 / Chapter 9.4 --- Current-mode approach --- p.80 / Chapter 9.4.1 --- "Site function, unit output function and a completed network" --- p.80 / Chapter 9.5 --- Conclusion --- p.83 / Chapter 10 --- Conclusions --- p.85 / Chapter 10.1 --- Summary of Achievements --- p.85 / Chapter 10.2 --- Future development --- p.88 / Chapter 10.2.1 --- Application for financial problems --- p.88 / Chapter 10.2.2 --- Fabrication of VLSI Implementation --- p.88 / Chapter 10.2.3 --- Actual prototyping of Analog Integrated Circuits for critical path and transitive closure problems --- p.89 / Chapter 10.2.4 --- Other implementation platform --- p.89 / Chapter 10.2.5 --- On-line update of routing table inside the router for network com- munication using BRIN --- p.89 / Chapter 10.2.6 --- Other BRIN's applications --- p.90 / Bibliography --- p.91

by Kwok-Fai Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 100-103). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.4 / Chapter 1.1 --- Summary of Contributions --- p.5 / Chapter 1.2 --- Organization of the Thesis --- p.6 / Chapter 2 --- Current Methods and Problems --- p.8 / Chapter 2.1 --- Statisticians --- p.8 / Chapter 2.1.1 --- ARMA --- p.8 / Chapter 2.1.1.1 --- Moving Average models --- p.8 / Chapter 2.1.1.2 --- Autoregressive models --- p.9 / Chapter 2.1.1.3 --- Stationary Process --- p.10 / Chapter 2.1.1.4 --- Autoregressive-Moving Average model --- p.10 / Chapter 2.1.1.5 --- Parameter Estimation --- p.11 / Chapter 2.2 --- Financial Researchers --- p.11 / Chapter 2.2.1 --- Efficient Market Theory --- p.11 / Chapter 2.3 --- Computer Scientists --- p.12 / Chapter 2.3.1 --- Expert System --- p.12 / Chapter 2.3.2 --- Neural Network --- p.14 / Chapter 2.3.2.1 --- Multilayer Perceptron --- p.14 / Chapter 2.3.2.2 --- Radial Basis Function Network (RBF) --- p.19 / Chapter 2.4 --- Research Apart from Prediction and Trading in Finance --- p.22 / Chapter 2.4.1 --- Derivatives Valuation and Hedging --- p.22 / Chapter 2.4.1.1 --- Volatility --- p.22 / Chapter 2.4.2 --- Pricing of Initial Public Offering --- p.24 / Chapter 2.4.3 --- Credit Rating --- p.25 / Chapter 2.4.4 --- Financial Health Assessment --- p.26 / Chapter 2.5 --- Discussion --- p.27 / Chapter 3 --- Correlation Basis Function Network --- p.28 / Chapter 3.1 --- Formulation of CBF network --- p.31 / Chapter 3.2 --- First Order Learning Algorithm --- p.32 / Chapter 3.3 --- Summary --- p.35 / Chapter 4 --- Applications of CBF Network in Stock trading --- p.36 / Chapter 4.1 --- Data Representation --- p.36 / Chapter 4.2 --- Data Pre-processing --- p.38 / Chapter 4.2.1 --- Input data pre-processing --- p.38 / Chapter 4.2.2 --- Output data pre-processing --- p.38 / Chapter 4.3 --- Multiple CBF Networks for Generation of Trading Signals --- p.41 / Chapter 4.4 --- Output Data Post-processing --- p.41 / Chapter 4.5 --- Trader's Interpretation --- p.43 / Chapter 4.6 --- Maximum profit trading system --- p.45 / Chapter 4.7 --- Performance Evaluation --- p.46 / Chapter 5 --- Applications of CBF Network in Warrant trading --- p.48 / Chapter 5.1 --- Option Model --- p.48 / Chapter 5.2 --- Warrant Model --- p.49 / Chapter 5.3 --- Black-Scholes Pricing Formula --- p.51 / Chapter 5.4 --- Using CBF Network for choosing warrants --- p.53 / Chapter 5.5 --- Trading System --- p.53 / Chapter 5.5.1 --- Trading System by Black-Scholes Model --- p.54 / Chapter 5.5.2 --- Trading System by Warrant Sensitivity --- p.55 / Chapter 5.6 --- Learning of Parameters in Warrant Sensitivity Model by Hierarchi- cal CBF Network --- p.57 / Chapter 5.7 --- Experimental Results --- p.59 / Chapter 5.7.1 --- Aggregate profit --- p.62 / Chapter 5.8 --- Summary and Discussion --- p.69 / Chapter 6 --- Analysis of CBF Network and other models --- p.72 / Chapter 6.1 --- Time and Space Complexity --- p.72 / Chapter 6.1.1 --- RBF Network --- p.72 / Chapter 6.1.2 --- CBF Network --- p.74 / Chapter 6.1.3 --- Black-Scholes Pricing Formula --- p.74 / Chapter 6.1.4 --- Warrant Sensitivity Model --- p.75 / Chapter 6.2 --- "Model Confidence, Prediction Confidence and Model Stability" --- p.76 / Chapter 6.2.1 --- Model and Prediction Confidence --- p.77 / Chapter 6.2.2 --- Model Stability --- p.77 / Chapter 6.2.3 --- Linear Model Analysis --- p.79 / Chapter 6.2.4 --- CBF Network Analysis --- p.82 / Chapter 6.2.5 --- Black-Scholes Pricing Formula Analysis --- p.84 / Chapter 7 --- Conclusion --- p.93 / Chapter 7.1 --- Neural Network and Statistical Modeling --- p.95 / Chapter 7.2 --- Financial Markets --- p.95 / Chapter A --- RBF Network Parameters Estimation --- p.101 / Chapter A.1 --- Least Squares --- p.101 / Chapter A.2 --- Gradient Descent Algorithm --- p.103 / Chapter B --- Further study on Black-Scholes Model --- p.104

Kei-Keung Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 127-128). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Static Portfolio Techniques --- p.1 / Chapter 1.2 --- Neural Network Approach --- p.2 / Chapter 1.3 --- Contributions of this Thesis --- p.3 / Chapter 1.4 --- Application of this Research --- p.4 / Chapter 1.5 --- Organization of this Thesis --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Standard Markowian Portfolio Optimization (SMPO) and Sharpe Ratio --- p.6 / Chapter 2.2 --- Downside Risk --- p.9 / Chapter 2.3 --- Augmented Lagrangian Method --- p.10 / Chapter 2.4 --- Adaptive Supervised Learning Decision (ASLD) System --- p.13 / Chapter I --- Static Portfolio Optimization Techniques --- p.19 / Chapter 3 --- Modified Portfolio Sharpe Ratio Maximization (MPSRM) --- p.20 / Chapter 3.1 --- Experiment Setting --- p.21 / Chapter 3.2 --- Downside Risk and Upside Volatility --- p.22 / Chapter 3.3 --- Investment Diversification --- p.24 / Chapter 3.4 --- Analysis of the Parameters H and B of MPSRM --- p.27 / Chapter 3.5 --- Risk Minimization with Control of Expected Return --- p.30 / Chapter 3.6 --- Return Maximization with Control of Expected Downside Risk --- p.32 / Chapter 4 --- Variations of Modified Portfolio Sharpe Ratio Maximization --- p.34 / Chapter 4.1 --- Soft-max Version of Modified Portfolio Sharpe Ratio Maximization (SMP- SRM) --- p.35 / Chapter 4.1.1 --- Applying Soft-max Technique to Modified Portfolio Sharpe Ratio Maximization (SMPSRM) --- p.35 / Chapter 4.1.2 --- Risk Minimization with Control of Expected Return --- p.37 / Chapter 4.1.3 --- Return Maximization with Control of Expected Downside Risk --- p.38 / Chapter 4.2 --- Soft-max Version of MPSRM with Entropy-like Regularization Term (SMPSRM-E) --- p.39 / Chapter 4.2.1 --- Using Entropy-like Regularization term in Soft-max version of Modified Portfolio Sharpe Ratio Maximization (SMPSRM-E) --- p.39 / Chapter 4.2.2 --- Risk Minimization with Control of Expected Return --- p.41 / Chapter 4.2.3 --- Return Maximization with Control of Expected Downside Risk --- p.43 / Chapter 4.3 --- Analysis of Parameters in SMPSRM and SMPSRM-E --- p.44 / Chapter II --- Neural Network Approach --- p.48 / Chapter 5 --- Investment on a Foreign Exchange Market using ASLD system --- p.49 / Chapter 5.1 --- Investment on A Foreign Exchange Portfolio --- p.49 / Chapter 5.2 --- Two Important Issues Revisited --- p.51 / Chapter 6 --- Investment on Stock market using ASLD System --- p.54 / Chapter 6.1 --- Investment on Hong Kong Hang Seng Index --- p.54 / Chapter 6.1.1 --- Performance of the Original ASLD System --- p.54 / Chapter 6.1.2 --- Performances After Adding Several Heuristic Strategies --- p.55 / Chapter 6.2 --- Investment on Six Different Stock Indexes --- p.61 / Chapter 6.2.1 --- Structure and Operation of the New System --- p.62 / Chapter 6.2.2 --- Experimental Results --- p.63 / Chapter III --- Combination of Static Portfolio Optimization techniques with Neural Network Approach --- p.67 / Chapter 7 --- Combining the ASLD system with Different Portfolio Optimizations --- p.68 / Chapter 7.1 --- Structure and Operation of the New System --- p.69 / Chapter 7.2 --- Combined with the Standard Markowian Portfolio Optimization (SMPO) --- p.70 / Chapter 7.3 --- Combined with the Modified Portfolio Sharpe Ratio Maximization (MP- SRM) --- p.72 / Chapter 7.4 --- Combined with the MPSRM ´ؤ Risk Minimization with Control of Ex- pected Return --- p.74 / Chapter 7.5 --- Combined with the MPSRM ´ؤ Return Maximization with Control of Expected Downside Risk --- p.76 / Chapter 7.6 --- Combined with the Soft-max Version of MPSRM (SMPSRM) --- p.77 / Chapter 7.7 --- Combined with the SMPSRM - Risk Minimization with Control of Ex- pected Return --- p.79 / Chapter 7.8 --- Combined with the SMPSRM ´ؤ Return Maximization with Control of Expected Downside Risk --- p.80 / Chapter 7.9 --- Combined with the Soft-max Version of MPSRM with Entropy-like Reg- ularization Term (SMPSRM-E) --- p.82 / Chapter 7.10 --- Combined with the SMPSRM-E ´ؤ Risk Minimization with Control of Expected Return --- p.84 / Chapter 7.11 --- Combined with the SMPSRM-E ´ؤ Return Maximization with Control of Expected Downside Risk --- p.86 / Chapter IV --- Software Developed --- p.93 / Chapter 8 --- Windows Application Developed --- p.94 / Chapter 8.1 --- Decision on Platform and Programming Language --- p.94 / Chapter 8.2 --- System Design --- p.96 / Chapter 8.3 --- Operation of our program --- p.97 / Chapter 9 --- Conclusion --- p.103 / Chapter A --- Algorithm for Portfolio Sharpe Ratio Maximization (PSRM) --- p.105 / Chapter B --- Algorithm for Improved Portfolio Sharpe Ratio Maximization (ISRM) --- p.107 / Chapter C --- Proof of Regularization Term Y --- p.109 / Chapter D --- Algorithm for Modified Portfolio Sharpe Ratio Maximization (MP- SRM) --- p.111 / Chapter E --- Algorithm for MPSRM with Control of Expected Return --- p.113 / Chapter F --- Algorithm for MPSRM with Control of Expected Downside Risk --- p.115 / Chapter G --- Algorithm for SMPSRM with Control of Expected Return --- p.117 / Chapter H --- Algorithm for SMPSRM with Control of Expected Downside Risk --- p.119 / Chapter I --- Proof of Entropy-like Regularization Term --- p.121 / Chapter J --- Algorithm for SMPSRM-E with Control of Expected Return --- p.123 / Chapter K --- Algorithm for SMPSRM-E with Control of Expected Downside Riskl25 Bibliography --- p.127