An approximate approach for training polynomial kernel SVMs in linear time
Yu‐Chieh Wu, Jie-Chi Yang, Yue‐Shi Lee · 2007
Kernel methods such as support vector machines (SVMs) have attracted a great deal of popularity in the machine learning and natural language processing (NLP) communities. Polynomial kernel SVMs showed very competitive accuracy in many NLP problems, like part-of-speech tagging and chunking. However, these methods are usually too inefficient to be applied to large dataset and real time purpose. In this paper, we propose an approximate method to analogy polynomial kernel with efficient data mining approaches. To prevent exponential-scaled testing time complexity, we also present a new method for speeding up SVM classifying which does independent to the polynomial degree d. The experimental results showed that our method is 16.94 and 450 times faster than traditional polynomial kernel in terms of training and testing respectively.