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대용량 패턴 인식 문제를 위한 서포트 함수 기반 커널 학습 방법론

대용량 패턴 인식 문제를 위한 서포트 함수 기반 커널 학습 방법론
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In the last decade, the kernel methods have contributed to significant advances in research areas such as statistics, probability theory, data mining, pattern recognition and articial intelligence with practical success in their application to regression, classication, clustering, ranking and visualization problems. By utilizing techniques and theories from optimization, statistics, and most importantly functional analysis in the feature space, kernel machines have provided principled ways of solving these problems with exibility, generality and accuracy. Among various kernel methods, Support Vector Machines (SVMs) and Gaussian Processes(GPs) have been extensively studied and applied to diverse problems in machine learning with state-of-the-art performance. However, as the size of data to be analyzed by these kernel machines grows explosively due to the development of automated data collecting, processing and storing techniques, the eciency has emerged as a critical issue. In this circumstance, nonlinear training cost of the kernel machines to solve quadratic programming for SVMs and eigendecomposition problem for GPs inhibits their practical advantages. And when the runtime complexity of the solution is a primary concern as in many real-time applications, these kernel methods are less attractive than the conventional linear models since the run time complexity of kernel machines are generally proportional to the size of training data. To this end, many researches in kernel methods have been recently devoted to the development of fast algorithms for ecient training and sparse kernel machines for online testing. Even though some of them achieved fair success, most of them leads to another issues to be solved such as local minimum, performance degrade and sensitivity to the setting. In this thesis, we aim to provide a new class of methods which enhance the eciency of conventional kernel machines without compensating the performance and robustness. By utilizing important concepts from nonlinear dynamics, the proposed methods are mainly based on the multi-basin system constructed by kernel support functions. These include reduced set construction method for sparser kernel machines, fast labeling method for clustering, domain described machine for ranking and support learning for pattern denoising. Through various experiments on synthetic and real-world problems, we show the e ectiveness of the proposed methods by comparing the performance with other related methods. We expect the proposed methods will pave the way for the kernel machines to be applied eciently to the emerging largescale applications such as high-denition image analysis, web documents ranking and gene database clustering.
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