Nonlinear Classification via Linear SVMs and Multi-Task Learning.
CIKM '14: 2014 ACM Conference on Information and Knowledge Management Shanghai China November, 2014(2014)
摘要
Kernel SVM is prohibitively expensive when dealing with large nonlinear data. While ensembles of linear classifiers have been proposed to address this inefficiency, these methods are time-consuming or lack robustness. We propose an efficient classifier for nonlinear data using a new iterative learning algorithm, which partitions the data into clusters, and then trains a linear SVM for each cluster. These two steps are combined into a graphical model, with the parameters estimated efficiently using the EM algorithm. During training, clustered multi-task learning is used to capture the relatedness among the multiple linear SVMs and avoid overfitting. Experimental results on benchmark datasets show that our method outperforms state-of-the-art methods. During prediction, it also obtains comparable classification performance to kernel SVM, with much higher efficiency.
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