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https://scholarbank.nus.edu.sg/handle/10635/13857
DC Field | Value | |
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dc.title | Improved kernel methods for classification | |
dc.contributor.author | DUAN KAIBO | |
dc.date.accessioned | 2010-04-08T10:37:13Z | |
dc.date.available | 2010-04-08T10:37:13Z | |
dc.date.issued | 2004-04-30 | |
dc.identifier.citation | DUAN KAIBO (2004-04-30). Improved kernel methods for classification. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/13857 | |
dc.description.abstract | Support vector machines (SVMs) and kernel-based methods have become popular for solving classification problems. Improving kernel methods for classification and providing some more clear guidelines for practical designers are the main focus of this thesis. Chapter 1 gives a review of some background knowledge and motivates the thesis.On the theoretical side, in Chapter 3 we develop for kernel logistic regression (KLR) a new fast and robust dual algorithm; in Chapter 4 we generalize KLR to the multiclass case and develop a decomposition algorithm for it; in Chapter 5 we develop a new binary-classifiers-based multiclass method which also can give a posteriori probability estimation.On the practical side, in Chapter 2 we evaluate some simple performance measures for tuning SVM hyperparameters; in Chapter 6 we compare some commonly used multiclass kernel methods. The results from these empirical studies provide useful guidelines for practical designers.Thus, this thesis contributes, theoretically and practically, in improving the kernel methods for classification, especially in posteriori probability estimation for classification. In Chapter 7 we conclude the thesis work and make recommendation for future research. | |
dc.language.iso | en | |
dc.subject | Kernel Classification Methods, Support Vector Machines (SVMs), Kernel Logistic Regresssion (KLR), Multiclass, Hyperparameter Tuning, Probability | |
dc.type | Thesis | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.contributor.supervisor | POO AUN NEOW | |
dc.contributor.supervisor | S SATHIYA KEERTHI | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Ph.D Theses (Open) |
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DuanKB.pdf | 1.34 MB | Adobe PDF | OPEN | None | View/Download |
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