@inproceedings{a620f0a0380248a8b9af89b08a843573,
title = "Adaptive weighted fusion of local kernel classifiers for effective pattern classification",
abstract = "The theoretical and practical virtual of local learning algorithms had been verified by the machine learning community. The selection of the proper local classifier, however, remains a challenging problem. Rather than selecting one single local classifier, in this paper, we propose to choose several local classifiers and use adaptive fusion strategy to alleviate the choice problem of the proper local classifier. Based on the fast and scalable local kernel support vector machine (FaLK-SVM), we adopt the self-adaptive weighting fusion method for combining local support vector machine classifiers (FaLK-SVMa), and provide two fusion methods, distance-based weighting (FaLK-SVMad) and rank-based weighting methods (FaLK-SVMar). Experimental results on fourteen UCI datasets and three large scale datasets show that FaLK-SVMa can chieve higher classification accuracy than FaLK-SVM.",
keywords = "Kernel method, classifier fusion, local learning, nearest neighbors, support vector machine",
author = "Shixin Yang and Wangmeng Zuo and Lei Liu and Yanlai Li and David Zhang",
year = "2012",
doi = "10.1007/978-3-642-24728-6\_9",
language = "英语",
isbn = "9783642247279",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "63--70",
booktitle = "Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers",
address = "德国",
note = "7th International Conference on Intelligent Computing, ICIC 2011 ; Conference date: 11-08-2011 Through 14-08-2011",
}