计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于自适应核参数优化的小波核相关向量机算法

高明哲,许爱强,张伟   

  1. (海军航空工程学院 科研部,山东 烟台 264001)
  • 收稿日期:2016-07-14 出版日期:2017-09-15 发布日期:2017-09-15
  • 作者简介:高明哲(1988—),男,博士研究生,主研方向为模式识别、故障诊断;许爱强,教授、博士;张伟,博士研究生。

Wavelet Kernel Relevance Vector Machine Algorithm Based on Adaptive Kernel Parameter Optimization

GAO Mingzhe,XU Aiqiang,ZHANG Wei   

  1. (Department of Scientific Research,Naval Aeronautical and Astronautical University,Yantai,Shandong 264001,China)
  • Received:2016-07-14 Online:2017-09-15 Published:2017-09-15

摘要: 传统相关向量机算法在处理大规模数据集时训练速度较慢,并且高斯径向核无法完备表示特征空间。为此,基于自适应核参数优化,提出一种小波核相关向量机算法。以小波核作为基函数,在训练中,采取增量学习流程实现各个小波核参数的快速自适应优化。将提出算法应用于混沌时间序列预测及UCI数据集分类实验,结果表明,自适应参数优化小波相关向量机算法在预测精度、训练速度上均优于传统相关向量机算法。

关键词: 相关向量机, 小波核函数, 自适应参数优化, 增量学习, 稀疏度先验

Abstract: The traditional Relevance Vector Machine(RVM) algorithm is slow to train on large scale datasets,and the Gauss radial kernel cannot express the feature space completely.So based on adaptive kernel parameter optimization,this paper proposes a wavelet kernel relevance vector machine algorithm.Regarding the wavelet kernel as the basis function,incremental learning process is used to realize the fast adaptive optimization of each wavelet kernel parameter in training.The proposed algorithm is used on prediction of chaotic time series and classification of UCI data sets.Simulation results show that the adaptive parameter optimization wavelet correlation vector machine algorithm is superior to the traditional correlation vector machine algorithm in forcasting accuracy and training speed.

Key words: Revelance Vector Machine(RVM), wavelet kernel function, adaptive parameter optimization, incremental leaning, sparsity prior

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