Yung Lit Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves p. [122]-124). / Text in English; abstracts in English and Chinese. / Yung Lit Hung. / Abstract --- p.i / Acknowledgements --- p.ii / List of Figures --- p.v / List of Tables --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Dissipative two-state system --- p.3 / Chapter 2.1 --- Introduction --- p.3 / Chapter 2.2 --- A two-state system viewed as a spin --- p.4 / Chapter 2.3 --- Rotation of spin operators --- p.5 / Chapter 2.4 --- Dissipative two state system --- p.7 / Chapter 2.5 --- The model in consideration --- p.8 / Chapter 2.5.1 --- gk= 0 --- p.8 / Chapter 2.5.2 --- Δ0 = 0 --- p.8 / Chapter 2.5.3 --- dispersionless phonon case with constant coupling --- p.10 / Chapter 3 --- Linearized spin-wave approximation and mean-field method --- p.13 / Chapter 3.1 --- Holstein Primakoff Transformation --- p.13 / Chapter 3.2 --- Application of linearized spin-wave approxmation to our system --- p.14 / Chapter 3.3 --- Mean-field method --- p.24 / Chapter 4 --- Variational method for optical phonons with constant coupling --- p.35 / Chapter 4.1 --- Introduction --- p.35 / Chapter 4.2 --- Variational Principle --- p.35 / Chapter 4.3 --- Variational Principle applied to optical phonon case --- p.36 / Chapter 4.4 --- Results --- p.41 / Chapter 4.5 --- Conclusion --- p.54 / Chapter 5 --- Variational method for acoustic phonons with ohmic dissipation --- p.56 / Chapter 5.1 --- Introduction --- p.56 / Chapter 5.2 --- Variational Principle applied to acoustic phonon case --- p.57 / Chapter 5.3 --- μk= 0 case --- p.59 / Chapter 5.4 --- Search for any μk≠ 0 solution --- p.60 / Chapter 5.5 --- Results --- p.62 / Chapter 5.6 --- Conclusion --- p.70 / Chapter 6 --- Coupled Cluster Method --- p.72 / Chapter 6.1 --- Introduction --- p.72 / Chapter 6.2 --- Coupled Cluster Method --- p.73 / Chapter 6.2.1 --- Zeroth Level --- p.74 / Chapter 6.2.2 --- First Level --- p.74 / Chapter 6.2.3 --- The bra-state --- p.75 / Chapter 6.3 --- Coupled cluster method applied to our system --- p.76 / Chapter 6.4 --- Coupled cluster method applied to optical phonon case --- p.78 / Chapter 6.4.1 --- First Level --- p.79 / Chapter 6.4.2 --- Second Level --- p.81 / Chapter 6.5 --- Coupled cluster method applied to acoustic phonon case --- p.90 / Chapter 6.5.1 --- First Level --- p.90 / Chapter 6.5.2 --- Second Level --- p.92 / Chapter 6.6 --- Conclusion --- p.98 / Chapter 7 --- Spin system interacting with a photon field --- p.99 / Chapter 7.1 --- Rotation wave approximation --- p.100 / Chapter 7.2 --- Spin system interacting with an optical field --- p.101 / Chapter 7.3 --- Heisenberg equation of motion --- p.102 / Chapter 7.4 --- Brogoliubov transformation approach --- p.104 / Chapter 7.5 --- Conclusion --- p.106 / Chapter A --- Supplementary calculations --- p.107 / Chapter A.1 --- First level calculation for optical photon --- p.107 / Chapter A.2 --- Second level calculation for optical photon --- p.111 / Chapter A.3 --- First level calculation for acoustic photon --- p.114 / Chapter A.4 --- Second level calculation for acoustic photon --- p.118 / Bibliography --- p.121

by Chan Wai To. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 117-121). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Clustering --- p.3 / Chapter 1.2 --- Mobile Agent --- p.4 / Chapter 1.3 --- Contribution --- p.4 / Chapter 1.4 --- Outline of this Thesis --- p.5 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Clustering --- p.6 / Chapter 2.1.1 --- K-Means Clustering --- p.6 / Chapter 2.1.2 --- A more efficient K-Means Clustering Algorithm --- p.3 / Chapter 2.1.3 --- K-Medoids Clustering Algorithms --- p.8 / Chapter 2.1.4 --- Linkage-based Methods --- p.11 / Chapter 2.1.5 --- BIRCH --- p.13 / Chapter 2.1.6 --- DBSCAN --- p.14 / Chapter 2.1.7 --- Other Clustering Algorithm --- p.17 / Chapter 2.2 --- Parallel Clustering and Distributed Clustering --- p.17 / Chapter 2.2.1 --- A Fast Parallel Clustering Algorithm for Large Spatial Databases --- p.17 / Chapter 2.3 --- Distributed Data Mining --- p.18 / Chapter 2.3.1 --- A Distributed Clustering Algorithm --- p.18 / Chapter 2.3.2 --- Efficient Mining of Association Rules in Distributed Databases --- p.19 / Chapter 2.4 --- Information Retrieval and Document Clustering --- p.20 / Chapter 2.4.1 --- Document and Document Set Representation --- p.20 / Chapter 2.4.2 --- TFIDF --- p.20 / Chapter 2.4.3 --- Similarity --- p.21 / Chapter 2.4.4 --- Partitional Document Clustering --- p.22 / Chapter 2.4.5 --- Hierarchical Document Clustering --- p.22 / Chapter 2.4.6 --- Document Clustering Application --- p.23 / Chapter 3 --- Distributed Clustering --- p.24 / Chapter 3.1 --- Problem Description --- p.24 / Chapter 3.2 --- Distributed k-Means Clustering Algorithm --- p.25 / Chapter 3.2.1 --- Initialization --- p.25 / Chapter 3.2.2 --- weighted k-Means procedure --- p.26 / Chapter 3.2.3 --- Refinement --- p.27 / Chapter 3.2.4 --- Example --- p.31 / Chapter 3.2.5 --- Communication Cost --- p.34 / Chapter 3.3 --- Grid k-Mean --- p.34 / Chapter 3.3.1 --- Runtime Splitting --- p.36 / Chapter 3.3.2 --- Initial Clusters --- p.38 / Chapter 3.3.3 --- Refinement --- p.38 / Chapter 3.3.4 --- Overall Algorithm --- p.39 / Chapter 3.3.5 --- Efficiency in Decomposition --- p.42 / Chapter 3.3.6 --- Example --- p.42 / Chapter 3.3.7 --- Comparison with previous k-Means method --- p.43 / Chapter 3.3.8 --- Communication Cost --- p.44 / Chapter 3.4 --- Experiment --- p.44 / Chapter 3.4.1 --- Performance --- p.46 / Chapter 3.4.2 --- Communication Cost --- p.47 / Chapter 3.4.3 --- Quality of Clustering --- p.49 / Chapter 3.4.4 --- Clustering in High Dimension --- p.49 / Chapter 3.4.5 --- Other Data Distributions --- p.52 / Chapter 4 --- Distributed DBSCAN --- p.54 / Chapter 4.1 --- Representative points of local candidate clusters --- p.55 / Chapter 4.2 --- Verification and Cluster Merging --- p.57 / Chapter 4.2.1 --- Clustering Result Quality --- p.59 / Chapter 4.3 --- Experiment --- p.62 / Chapter 5 --- Document Clustering --- p.72 / Chapter 5.1 --- Initialization --- p.73 / Chapter 5.2 --- Refinement --- p.76 / Chapter 5.3 --- Stopping criteria --- p.77 / Chapter 5.4 --- Message --- p.77 / Chapter 5.5 --- Algorithm --- p.78 / Chapter 5.6 --- Experiment --- p.82 / Chapter 5.6.1 --- Data Source and Experimental Setup --- p.82 / Chapter 5.6.2 --- Data Size --- p.34 / Chapter 5.6.3 --- Evaluation Metrics --- p.85 / Chapter 5.6.4 --- Experimental Result --- p.85 / Chapter 5.6.5 --- Comparison to Other Algorithms --- p.94 / Chapter 5.6.6 --- Conclusion --- p.94 / Chapter 5.7 --- Future Work --- p.95 / Chapter 6 --- Agent and Implementation Details --- p.96 / Chapter 6.1 --- Agent Introduction --- p.96 / Chapter 6.1.1 --- Reason to use Mobile Agent --- p.97 / Chapter 6.1.2 --- Grasshopper Overview --- p.97 / Chapter 6.1.3 --- Agent Scenario --- p.98 / Chapter 6.1.4 --- Another Agent Scenario --- p.99 / Chapter 6.2 --- Implementation Details --- p.100 / Chapter 6.2.1 --- Distributed k-Means --- p.100 / Chapter 6.2.2 --- Grid k-Means --- p.104 / Chapter 6.2.3 --- Distributed DBSCAN --- p.109 / Chapter 6.2.4 --- Distributed Document Clustering --- p.112 / Chapter 7 --- Conclusion

Kwok Chi Leong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 122-127). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgment --- p.vi / Table of Contents --- p.vii / List of Tables --- p.x / List of Figures --- p.xi / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Contributions --- p.2 / Chapter 1.2 --- Thesis Overview --- p.3 / Chapter Chapter 2 --- Background Information --- p.5 / Chapter 2.1 --- Documents Clustering --- p.5 / Chapter 2.1.1 --- Review of Clustering Techniques --- p.5 / Chapter 2.1.2 --- Suffix Tree Clustering --- p.7 / Chapter 2.2 --- Chinese Information Processing --- p.8 / Chapter 2.2.1 --- Sentence Segmentation --- p.8 / Chapter 2.2.2 --- Keyword Extraction --- p.10 / Chapter Chapter 3 --- The Generic Chinese PAT Tree --- p.12 / Chapter 3.1 --- PAT Tree --- p.13 / Chapter 3.1.1 --- Patricia Tree --- p.13 / Chapter 3.1.2 --- Semi-Infinite String --- p.14 / Chapter 3.1.3 --- Structure of Tree Nodes --- p.17 / Chapter 3.1.4 --- Some Examples of PAT Tree --- p.22 / Chapter 3.1.5 --- Storage Complexity --- p.24 / Chapter 3.2 --- The Chinese PAT Tree --- p.26 / Chapter 3.2.1 --- The Chinese PAT Tree Structure --- p.26 / Chapter 3.2.2 --- Some Examples of Chinese PAT Tree --- p.30 / Chapter 3.2.3 --- Storage Complexity --- p.33 / Chapter 3.3 --- The Generic Chinese PAT Tree --- p.34 / Chapter 3.3.1 --- Structure Overview --- p.34 / Chapter 3.3.2 --- Structure of Tree Nodes --- p.35 / Chapter 3.3.3 --- Essential Node --- p.37 / Chapter 3.3.4 --- Some Examples of the Generic Chinese PAT Tree --- p.41 / Chapter 3.3.5 --- Storage Complexity --- p.45 / Chapter 3.4 --- Problems of Embedded Nodes --- p.46 / Chapter 3.4.1 --- The Reduced Structure --- p.47 / Chapter 3.4.2 --- Disadvantages of Reduced Structure --- p.48 / Chapter 3.4.3 --- A Case Study of Reduced Design --- p.50 / Chapter 3.4.4 --- Experiments on Frequency Mismatch --- p.51 / Chapter 3.5 --- Strengths of the Generic Chinese PAT Tree --- p.55 / Chapter Chapter 4 --- Performance Analysis on the Generic Chinese PAT Tree --- p.58 / Chapter 4.1 --- The Construction of the Generic Chinese PAT Tree --- p.59 / Chapter 4.2 --- Counting the Essential Nodes --- p.61 / Chapter 4.3 --- Performance of Various PAT Trees --- p.62 / Chapter 4.4 --- The Implementation Analysis --- p.64 / Chapter 4.4.1 --- Pure Dynamic Memory Allocation --- p.64 / Chapter 4.4.2 --- Node Production Factory Approach --- p.66 / Chapter 4.4.3 --- Experiment Result of the Factory Approach --- p.68 / Chapter Chapter 5 --- The Chinese Documents Clustering --- p.70 / Chapter 5.1 --- The Clustering Framework --- p.70 / Chapter 5.1.1 --- Documents Cleaning --- p.73 / Chapter 5.1.2 --- PAT Tree Construction --- p.76 / Chapter 5.1.3 --- Essential Node Extraction --- p.77 / Chapter 5.1.4 --- Base Clusters Detection --- p.80 / Chapter 5.1.5 --- Base Clusters Filtering --- p.86 / Chapter 5.1.6 --- Base Clusters Combining --- p.94 / Chapter 5.1.7 --- Documents Assigning --- p.95 / Chapter 5.1.8 --- Result Presentation --- p.96 / Chapter 5.2 --- Discussion --- p.96 / Chapter 5.2.1 --- Flexibility of Our Framework --- p.96 / Chapter 5.2.2 --- Our Clustering Model --- p.97 / Chapter 5.2.3 --- More About Clusters Detection --- p.98 / Chapter 5.2.4 --- Analysis and Complexity --- p.100 / Chapter Chapter 6 --- Evaluations on the Chinese Documents Clustering --- p.101 / Chapter 6.1 --- Details of Experiment --- p.101 / Chapter 6.1.1 --- Parameter of Weighted Frequency --- p.105 / Chapter 6.1.2 --- Effect of CLP Analysis --- p.105 / Chapter 6.1.3 --- Result of Clustering --- p.108 / Chapter 6.2 --- Clustering on Larger Collection --- p.109 / Chapter 6.2.1 --- Comparing the Base Clusters --- p.109 / Chapter 6.2.2 --- Result of Clustering --- p.111 / Chapter 6.2.3 --- Discussion --- p.112 / Chapter 6.3 --- Clustering with Part of Documents --- p.113 / Chapter 6.3.1 --- Clustering with News Headlines --- p.114 / Chapter 6.3.2 --- Clustering with News Abstract --- p.117 / Chapter Chapter 7 --- Conclusion --- p.119 / Bibliography --- p.122

The ground water quality of the Trinity Aquifer for wells sampled between 2000 and 2009 was examined using multivariate and spatial statistical techniques. A Kruskal-Wallis test revealed that all of the water quality parameters with the exception of nitrate vary with land use. A Spearman’s rho analysis illustrates that every water quality parameter with the exception of silica correlated with well depth. Factor analysis identified four factors contributable to hydrochemical processes, electrical conductivity, alkalinity, and the dissolution of parent rock material into the ground water. The cluster analysis generated seven clusters. A chi-squared analysis shows that Clusters 1, 2, 5, and 6 are reflective of the distribution of the entire dataset when looking specifically at land use categories. The nearest neighbor analysis revealed clustered, dispersed, and random patterns depending upon the entity being examined. The spatial autocorrelation technique used on the water quality parameters for the entire dataset identified that all of the parameters are random with the exception of pH which was found to be spatially clustered. The combination of the multivariate and spatial techniques together identified influences on the Trinity Aquifer including hydrochemical processes, agricultural activities, recharge, and land use. In addition, the techniques aided in identifying areas warranting future monitoring which are located in the western and southwestern parts of the aquifer.

This thesis consists of three papers in applied statistics, specifically in cluster sampling, causal inference, and measurement error. The first paper studies the problem of estimating the finite population mean from a two-stage sample with unequal selection probabilies in a Bayesian framework. Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. In a two-stage cluster sampling design, clusters are first selected with probability proportional to cluster size, and units are then randomly sampled within selected clusters. Methodological challenges arise when the sizes of nonsampled cluster are unknown. We propose both nonparametric and parametric Bayesian approaches for predicting the cluster size, and we implement inference for the unknown cluster sizes simultaneously with inference for survey outcome. We implement this method in Stan and use simulation studies to compare the performance of an integrated Bayesian approach to classical methods on their frequentist properties. We then apply our propsed method to the Fragile Families and Child Wellbeing study as an illustration of complex survey inference. The second paper focuses on the problem of weak instrumental variables, motivated by estimating the causal effect of incarceration on recidivism. An instrument is weak when it is only weakly predictive of the treatment of interest. Given the well-known pitfalls of weak instrumental variables, we propose a method for strengthening a weak instrument. We use a matching strategy that pairs observations to be close on observed covariates but far on the instrument. This strategy strengthens the instrument, but with the tradeoff of reduced sample size. To help guide the applied researcher in selecting a match, we propose simulating the power of a sensitivity analysis and design sensitivity and using graphical methods to examine the results. We also demonstrate the use of recently developed methods for identifying effect modification, which is an interaction between a pretreatment covariate and the treatment. Larger and less variable treatment effects are less sensitive to unobserved bias, so identifying when effect modification is present and which covariates may be the source is important. We undertake our study in the context of studying the causal effect of incarceration on recividism via a natural experiment in the state of Pennsylvania, a motivating example that illustrates each component of our analysis. The third paper considers the issue of measurement error in the context of survey sampling and hierarchical models. Researchers are often interested in studying the relationship between community-levels variables and individual outcomes. This approach often requires estimating the neighborhood-level variable of interest from the sampled households, which induces measurement error in the neighborhood-level covariate since not all households are sampled. Other times, neighborhood-level variables are not observed directly, and only a noisy proxy is available. In both cases, the observed variables may contain measurement error. Measurement error is known to attenuate the coefficient of the mismeasured variable, but it can also affect other coefficients in the model, and ignoring measurement error can lead to misleading inference. We propose a Bayesian hierarchical model that integrates an explicit model for the measurement error process along with a model for the outcome of interest for both sampling-induced measurement error and classical measurement error. Advances in Bayesian computation, specifically the development of the Stan probabilistic programming language, make the implementation of such models easy and straightforward.

Purpose – The financial systems in many emerging countries are still impacted by the devastating effect of the 2008 financial crisis which created a massive disaster in the global economy. The banking sector needs appropriate quantitative techniques to assess its financial soundness, strengths and weaknesses. This research aims to explore, empirically assess and analyze the financial soundness of the banking sector in Kazakhstan. It also examines the prediction of financial unsoundness at an individual bank level using PCA, cluster, MDA, logit and probit analyses. Design/Methodology/Approach – A cluster analysis, in combination with principal component analysis (PCA), was utilized as a classification technique. It groups sound and unsound banks in Kazakhstan's banking sector by examining various financial ratios. Cluster analysis was run on a sample of 34 commercial banks on 1st January, 2008 and 37 commercial banks on 1st January, 2014 to test the ability of this technique to detect unsound banks before they fail. Then, Altman Z” and EM Score models were tested and re-estimated and the MDA, logit and probit models were constructed on a sample of 12 Kazakhstan banks during the period between 1st January, 2008 and 1st January, 2014. The sample consists of 6 sound and 6 unsound banks and accounts for 81.3% of the total assets of the Kazakhstan banking sector in 2014. These statistical methods used various financial variables to represent capital adequacy, asset quality, management, earnings and liquidity. Last but not least, the MDA, logit and probit models were systematically combined together to construct an integrated model to predict bank financial unsoundness. Findings – First of all, results from Chapter 3 indicate that cluster analysis is able to identify the structure of the Kazakh banking sector by the degree of financial soundness. Secondly, based on the findings in the second empirical chapter, the tested and re-estimated Altman models show a modest ability to predict bank financial unsoundness in Kazakhstan. Thirdly, the MDA, logit and probit models show high predictive accuracy in excess of 80%. Finally, the model that integrated the MDA, logit and probit types presents superior predictability with lower Type I errors. Practical Implications – The results of this research are of interest to supervisory and regulatory bodies. The models can be used as a reliable and effective tool, particularly the cluster based methodology for assessing the degree of financial soundness in the banking sector and the integrated model for predicting the financial unsoundness of banks. Originality/Value – This study is the first to employ a cluster-based methodology to assess financial soundness in the Kazakh banking sector. In addition, the integrated model can be used as a promising technique for evaluating the financial unsoundness of banks in terms of predictive accuracy and robustness. Importance – Assessing the financial soundness of the Kazakh banking system is of particular importance as the World Bank has ranked Kazakhstan as leading the world for the volume of non-performing credits in the total number of loans granted in 2012. It is one of the first academic studies carried out on Kazakhstan banks which comprehensively evaluate the financial soundness of banks. It is anticipated that the findings of the current study will provide useful lessons for developing and transition countries during periods of financial turmoil.

Most existing semi-supervised document clustering approaches are model-based clustering and can be treated as parametric model taking an assumption that the underlying clusters follow a certain pre-defined distribution. In our semi-supervised document clustering, each cluster is represented by a non-parametric probability distribution. Two approaches are designed for incorporating pairwise constraints in the document clustering approach. The first approach, term-to-term relationship approach (TR), uses pairwise constraints for capturing term-to-term dependence relationships. The second approach, linear combination approach (LC), combines the clustering objective function with the user-provided constraints linearly. Extensive experimental results show that our proposed framework is effective. / This thesis presents a new framework for automatically partitioning text documents taking into consideration of constraints given by users. Semi-supervised document clustering is developed based on pairwise constraints. Different from traditional semi-supervised document clustering approaches which assume pairwise constraints to be prepared by user beforehand, we develop a novel framework for automatically discovering pairwise constraints revealing the user grouping preference. Active learning approach for choosing informative document pairs is designed by measuring the amount of information that can be obtained by revealing judgments of document pairs. For this purpose, three models, namely, uncertainty model, generation error model, and term-to-term relationship model, are designed for measuring the informativeness of document pairs from different perspectives. Dependent active learning approach is developed by extending the active learning approach to avoid redundant document pair selection. Two models are investigated for estimating the likelihood that a document pair is redundant to previously selected document pairs, namely, KL divergence model and symmetric model. / Huang, Ruizhang. / Adviser: Wai Lam. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3600. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 117-123). / 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, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

by Huidong Jin. / "August 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 193-204). / 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.

by Yiu Ming Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 92-99). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Bayesian YING-YANG Learning Theory and Number Selec- tion Criterion --- p.5 / Chapter 1.2 --- General Motivation --- p.6 / Chapter 1.3 --- Contributions of the Thesis --- p.6 / Chapter 1.4 --- Other Related Contributions --- p.7 / Chapter 1.4.1 --- A Fast Number Detection Approach --- p.7 / Chapter 1.4.2 --- Application of RPCL to Prediction Models for Time Series Forecasting --- p.7 / Chapter 1.4.3 --- Publications --- p.8 / Chapter 1.5 --- Outline of the Thesis --- p.8 / Chapter 2 --- Open Problem: How Many Clusters? --- p.11 / Chapter 3 --- Bayesian YING-YANG Learning Theory: Review and Experiments --- p.17 / Chapter 3.1 --- Briefly Review of Bayesian YING-YANG Learning Theory --- p.18 / Chapter 3.2 --- Number Selection Criterion --- p.20 / Chapter 3.3 --- Experiments --- p.23 / Chapter 3.3.1 --- Experimental Purposes and Data Sets --- p.23 / Chapter 3.3.2 --- Experimental Results --- p.23 / Chapter 4 --- Conditions of Number Selection Criterion --- p.39 / Chapter 4.1 --- Alternative Condition of Number Selection Criterion --- p.40 / Chapter 4.2 --- Conditions of Special Hard-cut Criterion --- p.45 / Chapter 4.2.1 --- Criterion Conditions in Two-Gaussian Case --- p.45 / Chapter 4.2.2 --- Criterion Conditions in k*-Gaussian Case --- p.59 / Chapter 4.3 --- Experimental Results --- p.60 / Chapter 4.3.1 --- Purpose and Data Sets --- p.60 / Chapter 4.3.2 --- Experimental Results --- p.63 / Chapter 4.4 --- Discussion --- p.63 / Chapter 5 --- Application of Number Selection Criterion to Data Classification --- p.80 / Chapter 5.1 --- Unsupervised Classification --- p.80 / Chapter 5.1.1 --- Experiments --- p.81 / Chapter 5.2 --- Supervised Classification --- p.82 / Chapter 5.2.1 --- RBF Network --- p.85 / Chapter 5.2.2 --- Experiments --- p.86 / Chapter 6 --- Conclusion and Future Work --- p.89 / Chapter 6.1 --- Conclusion --- p.89 / Chapter 6.2 --- Future Work --- p.90 / Bibliography --- p.92 / Chapter A --- A Number Detection Approach for Equal-and-Isotropic Variance Clusters --- p.100 / Chapter A.1 --- Number Detection Approach --- p.100 / Chapter A.2 --- Demonstration Experiments --- p.102 / Chapter A.3 --- Remarks --- p.105 / Chapter B --- RBF Network with RPCL Approach --- p.106 / Chapter B.l --- Introduction --- p.106 / Chapter B.2 --- Normalized RBF net and Extended Normalized RBF Net --- p.108 / Chapter B.3 --- Demonstration --- p.110 / Chapter B.4 --- Remarks --- p.113 / Chapter C --- Adaptive RPCL-CLP Model for Financial Forecasting --- p.114 / Chapter C.1 --- Introduction --- p.114 / Chapter C.2 --- Extraction of Input Patterns and Outputs --- p.115 / Chapter C.3 --- RPCL-CLP Model --- p.116 / Chapter C.3.1 --- RPCL-CLP Architecture --- p.116 / Chapter C.3.2 --- Training Stage of RPCL-CLP --- p.117 / Chapter C.3.3 --- Prediction Stage of RPCL-CLP --- p.122 / Chapter C.4 --- Adaptive RPCL-CLP Model --- p.122 / Chapter C.4.1 --- Data Pre-and-Post Processing --- p.122 / Chapter C.4.2 --- Architecture and Implementation --- p.122 / Chapter C.5 --- Computer Experiments --- p.125 / Chapter C.5.1 --- Data Sets and Experimental Purpose --- p.125 / Chapter C.5.2 --- Experimental Results --- p.126 / Chapter C.6 --- Conclusion --- p.134 / Chapter D --- Publication List --- p.135 / Chapter D.1 --- Publication List --- p.135

Li, Qiuhui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 46-47). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- An overview of the BitTorrent protocol --- p.3 / Chapter 2 --- Problem Formulation --- p.7 / Chapter 2.1 --- Type-based Peer Selection Algorithm --- p.7 / Chapter 2.2 --- Clustering Index --- p.9 / Chapter 3 --- Model Formulation --- p.11 / Chapter 3.1 --- Markovian Model --- p.12 / Chapter 3.2 --- Transition Matrix --- p.14 / Chapter 3.2.1 --- Search Process --- p.16 / Chapter 3.2.2 --- Match Process --- p.18 / Chapter 3.2.3 --- Cut Process --- p.19 / Chapter 3.3 --- Open System Model --- p.21 / Chapter 4 --- Numerical Results and Observations --- p.24 / Chapter 4.1 --- Clustering Index --- p.24 / Chapter 4.2 --- Upload Utilization --- p.26 / Chapter 4.3 --- Download Rate --- p.28 / Chapter 4.4 --- Open System --- p.30 / Chapter 5 --- Performance Evaluation --- p.32 / Chapter 5.1 --- Model Verification --- p.34 / Chapter 5.2 --- Control the Clustering Index --- p.36 / Chapter 6 --- Related Works --- p.40 / Chapter 7 --- Conclusions --- p.44 / Bibliography --- p.46