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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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Copyright 2004 Paul Komarek, komarek@cmu.edu