文献类型：会议论文

英文题名：Multiple kernel boosting framework based on information measure for classification

作者：Qi, Chengming[1,2];Wang, Yuping[1];Tian, Wenjie[1];Wang, Qun[2]

第一作者：Qi, Chengming;亓呈明

通讯作者：Qi, CM[1]

机构：[1]Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China;[2]China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：International Conference on Nonlinear Dynamics and Complexity

会议日期：MAY 11-15, 2015

会议地点：SPAIN

语种：英文

外文关键词：Classification; Ensemble learning; Kullback-Leibler distance; Multiple kernel learning (MKL)

摘要：The performance of kernel-based method, such as support vector machine (SVM), is greatly affected by the choice of kernel function. Multiple kernel learning (MKL) is a promising family of machine learning algorithms and has attracted many attentions in recent years. MKL combines multiple sub-kernels to seek better results compared to single kernel learning. In order to improve the efficiency of SVM and MKL, in this paper, the Kullback-Leibler kernel function is derived to develop SVM. The proposed method employs an improved ensemble learning framework, named KLMKB, which applies Adaboost to learning multiple kernel-based classifier. In the experiment for hyperspectral remote sensing image classification, we employ feature selected through Optional Index Factor (OIF) to classify the satellite image. We extensively examine the performance of our approach in comparison to some relevant and state-of-theart algorithms on a number of benchmark classification data sets and hyperspectral remote sensing image data set. Experimental results show that our method has a stable behavior and a noticeable accuracy for different data set. (C) 2015 Published by Elsevier Ltd.