Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Effect of term distributions on centroid-based text categorization
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Gradient-Based Adaptation of General Gaussian Kernels
Neural Computation
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
A Family-Based Evolutional Approach for Kernel Tree Selection in SVMs
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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Text categorization is one of the most interesting topic, due to the extremely increase of digital documents. The Support Vector Machine algorithm (SVM) is one of the most effective technique for solving this problem. However, SVM requires the user to choose the kernel function and parameters of the function, which directly effect to the performance of the classifiers. This paper proposes a novel method, named Kernel Tree SVM, which represents the multiple kernel function with a tree structure. The functions are selected and formed by using genetic programming (GP). Moreover, the gradient descent method is used to perform fine tune on parameter values in each tree. The method is benchmarked on WebKB and 20Newsgroup datasets. The results prove that the method can find a bettr optimal solution than the SVM tuned with the gradient method.