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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Feature selection and classification problem in bioinformatics.

January 2010 (has links)
Lau, Siu Him. / "November 2009." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.7 / Chapter 2 --- Support Vector Machine --- p.9 / Chapter 2.1 --- Two-Class Support Vector Machine --- p.9 / Chapter 2.2 --- Kernel Tricks --- p.12 / Chapter 2.3 --- Weighted Support Vector Machine --- p.14 / Chapter 2.4 --- Parameter Selection in Support Vector Machine --- p.16 / Chapter 3 --- Feature Selection Methods --- p.17 / Chapter 3.1 --- Principle Component Analysis --- p.17 / Chapter 3.1.1 --- Maximizing Variance --- p.17 / Chapter 3.1.2 --- Relation with Singular Value Decomposition --- p.19 / Chapter 3.1.3 --- Feature Selection by Singular Value Decomposition --- p.20 / Chapter 3.1.4 --- Disadvantage of Unsupervised Learning --- p.21 / Chapter 3.2 --- Linear Discriminant Analysis --- p.21 / Chapter 3.2.1 --- Between-class Distance and Within-class Variance --- p.22 / Chapter 3.2.2 --- Generalized Eigenvalue Problem --- p.24 / Chapter 3.2.3 --- Feature Selection by Linear Discriminant Analysis --- p.25 / Chapter 4 --- Application on a Real Problem --- p.27 / Chapter 4.1 --- Problem and Goals --- p.27 / Chapter 4.2 --- Diabetes Data Set --- p.27 / Chapter 4.3 --- Data Processing --- p.28 / Chapter 4.3.1 --- Pre-processing for Categorical Data --- p.28 / Chapter 4.3.2 --- Handling Uneven Data Set --- p.31 / Chapter 5 --- Results on Simulated and Real Data --- p.33 / Chapter 5.1 --- Evaluation --- p.33 / Chapter 5.1.1 --- Training and Testing --- p.33 / Chapter 5.1.2 --- Evaluation Method --- p.34 / Chapter 5.2 --- Classification Procedure --- p.35 / Chapter 5.3 --- Performance on Simulated Data --- p.36 / Chapter 5.4 --- Results on a Real Data Set --- p.39 / Chapter 5.4.1 --- Features Selection --- p.39 / Chapter 5.4.2 --- Performance on the Real Data Set --- p.41 / Chapter 5.4.3 --- Analysis on Risk Factors --- p.42 / Chapter 6 --- Conclusion --- p.44 / Bibliography --- p.46

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