Seminar: Self-Tuning One-Class Support Vector Machines for Data Classification

Yiming Qian
M.Sc. Candidate
Supervisor: Dr. Minglun Gong

Self-Tuning One-Class Support Vector Machines for Data Classification

Department of Computer Science
Thursday, April 10, 2014, 11:30 a.m., Room EN 2022


Abstract

Support Vector Machine (SVM) based classifiers are most popular approaches for data classification in machine learning. To obtain high classification accuracy, parameter tuning methods such as cross-validation are often applied. However, parameter tuning is a very time consuming process. To address this problem, a simple, efficient and parameter-free approach is proposed for both binary and multiclass classification problems in this thesis, and is especially useful when dealing with large-scale datasets in the presence of label noise. Grown out of one-class SVM, our approach enjoys several distinct features: First, by utilizing the advantage of online learning, it works well with dynamic data; Second, 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; Third, the internal parameters and especially the kernel bandwidth in the training process are self-tuned, which makes our approach handy to use even for first-time users.

The proposed approach is compared side-by-side with LIBSVM, arguably the most widely-used data classification system, in a sequence of empirical evaluations, where our approach is shown to perform almost as well as their optimal parameter settings tuned for individual datasets, while consuming only a fraction of the processing time. In addition, real-world problems in computer vision - namely foreground segmentation and boundary matting for live videos - are solved to demonstrate the ability on working on dynamic and large-scale datasets. Our approach is shown to be particularly competent at processing a wide range of videos under complex scenes, and near real-time processing speed (14 frames per second (FPS) without matting and 8 FPS with matting on a mid-range PC & GPU) is achieved for VGA-sized videos.

*Key Words:* Data Classification, Support Vector Machine, One-Class SVM, Parameter Free, Label Noise, Foreground Segmentation, Video Matting

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