title: A linear-RBF multikernel SVM to classify big text corpora.
abstract: Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.
authors: Department of Computer Science, Higher Technical School of Computer Engineering, University of Vigo, 32004 Ourense, Spain.
authors: R
authors: R
authors: Romero
authors: Department of Computer Science, Higher Technical School of Computer Engineering, University of Vigo, 32004 Ourense, Spain.
authors: E L
authors: EL
authors: Iglesias
authors: Department of Computer Science, Higher Technical School of Computer Engineering, University of Vigo, 32004 Ourense, Spain.
authors: L
authors: L
authors: Borrajo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D060388:Support Vector Machine