Self-tuning One-class Support Vector Machines for Data Classification
Author | : Yiming Qian |
Publisher | : |
Total Pages | : |
Release | : 2014 |
Genre | : |
ISBN | : |
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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.