tailieunhanh - Báo cáo hóa học: " The Optimal Design of Weighted Order Statistics Filters by Using Support Vector Machines"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: The Optimal Design of Weighted Order Statistics Filters by Using Support Vector Machines | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 24185 Pages 1-13 DOI ASP 2006 24185 The Optimal Design of Weighted Order Statistics Filters by Using Support Vector Machines Chih-Chia Yao and Pao-Ta Yu Department of Computer Science and Information Engineering College of Engineering National Chung Cheng University Chia-yi 62107 Taiwan Received 10 January 2005 Revised 13 September 2005 Accepted 7 November 2005 Recommended for Publication by Moon Gi Kang Support vector machines SVMs a classification algorithm for the machine learning community have been shown to provide higher performance than traditional learning machines. In this paper the technique of SVMs is introduced into the design of weighted order statistics WOS filters. WOS filters are highly effective in processing digital signals because they have a simple window structure. However due to threshold decomposition and stacking property the development of WOS filters cannot significantly improve both the design complexity and estimation error. This paper proposes a new designing technique which can improve the learning speed and reduce the complexity of designing WOS filters. This technique uses a dichotomous approach to reduce the Boolean functions from 255 levels to two levels which are separated by an optimal hyperplane. Furthermore the optimal hyperplane is gotten by using the technique of SVMs. Our proposed method approximates the optimal weighted order statistics filters more rapidly than the adaptive neural filters. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Support vector machines SVMs a classification algorithm for the machine learning community have attracted much attention in recent years 1-5 . In many applications SVMs have been shown to provide higher performance than traditional learning machines 6-8 . The principle of SVMs is based on approximating structural risk minimization. It shows that the