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A Parallelized Learning Algorithm for Monotonicity Constrained Support Vector Machines

Authors:
Hui-Chi Chuang
Chih-Chuan Chen
Chi Chou
Yi-Chung Cheng
Sheng-Tun Li

Keywords: support vector machines; monotonic prior knowledge; learning algorithm; parallel strategy

Abstract:
Various efforts have been made to improve support vector machines (SVMs) based on different scenarios of real world problems. SVMs are the so-called benchmarking neural network technology motivated by the results of statistical learning theory. Among them, taking into account experts' knowledge has been confirmed to help SVMs deal with noisy data to obtain more useful results. For example, SVMs with monotonicity constraints and with the Tikhonov regularization method, also known as Regularized Monotonic SVM (RMC-SVM) incorporate inequality constraints into SVMs based on the monotonic property of real-world problems and the Tikhonov regularization method is further applied to ensure that the solution is unique and bounded. These kinds of SVMs are also referred to as knowledge-oriented SVMs. However, solving SVMs with monotonicity constraints will require even more computation than SVMs. In this research, a parallelized learning strategy is proposed to solve the regularized monotonicity constrained SVMs. Due to the characteristics of the parallelized learning method, the dataset can be divided into several parts for parallel computing at different times. This study proposes a RMC-SVMs with a parallel strategy to reduce the required training time and to increase the feasibility of using RMC-SVMs in real world applications.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2017

Publication date: June 25, 2017

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-566-1

Location: Venice, Italy

Dates: from June 25, 2017 to June 29, 2017