Self-tuning one-class support vector machines for data classification

Qian, Yiming (2014) Self-tuning one-class support vector machines for data classification. Masters thesis, Memorial University of Newfoundland.

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Abstract

Support Vector Machine (SVM) based classifiers are most popular models for data classification in machine learning. To obtain high classification accuracy, parameter tuning methods such as cross-validation are often applied, which is however timeconsuming. To address this problem, a simple, efficient and parameter-free algorithm is presented in this thesis. The algorithm is especially useful when dealing with datasets in the presence of label noise. Grown out of one-class SVM, the presented algorithm enjoys several distinct features: First, its decision boundary is learned based on both positive and negative examples, whereas the original one-class SVM training is only based on positive examples; Second, the internal parameters are self-tuned, which makes the algorithm handy to use even for first-time users. Compared with the benchmark method LIBSVM, the presented algorithm achieves comparable accuracy, while consuming only a fraction of the processing time. Applications in computer vision are presented to demonstrate the effectiveness of the algorithm.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/8306
Item ID: 8306
Additional Information: Including bibliographical references (pages 58-66).
Department(s): Science, Faculty of > Computer Science
Date: June 2014
Date Type: Submission
Library of Congress Subject Heading: Support vector machines; Computer algorithms; Text processing (Computer science); Pattern recognition systems; Computer vision

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