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1.3 Organization of Thesis

This thesis is divided into four parts. Following this introductory part is Part II, which presents background information on the algorithms we use. Chapter 2 describes the linear and nonlinear Conjugate Gradient algorithms, Chapter 3 describes linear regression, and Chapter 4 describes logistic regression. After this, Part III discusses the use of LR for fast classification. This part is divided into Chapter 5, which discusses computational challenges, approaches, and conclusions as well as the datasets, scoring methods, and computing platform used for experiments; and Chapter 6 wherein we characterize our three LR variants, support vector machines, k-nearest-neighbor, and Bayes' classifier.

Part IV contains four short chapters. These begin with Chapter 7, which covers research related to this thesis. Chapter 8 is a summary of this thesis, including the conclusions we have drawn from our research. Chapter 9 describes our contributions, and Chapter 10 suggests several ideas for related future work. Following these concluding chapters are several appendices. We acknowledge several important people and organizations in Appendix A. Appendix B reproduces the documentation for the Auton Fast Classifier software [19] used in this thesis. Appendix C contains some light-hearted, miscellaneous information which didn't fit elsewhere. Concluding this thesis is the bibliography.


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Next: 2 Background Up: 1. Introduction Previous: 1.2 Focus Of Thesis   Contents
Copyright 2004 Paul Komarek, komarek@cmu.edu