Constructing the gene regulation-level representation of microarray data for cancer classification
Journal of Biomedical Informatics
LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Separating hypersurfaces of SVMs in input spaces
Pattern Recognition Letters
On the sparseness of 1-norm support vector machines
Neural Networks
Kernel matching reduction algorithms for classification
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Density-induced margin support vector machines
Pattern Recognition
A computational intelligence scheme for the prediction of the daily peak load
Applied Soft Computing
Hidden space principal component analysis
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A modified large margin classifier in hidden space for face recognition
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Nonlinear nearest subspace classifier
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A fast algorithm for kernel 1-norm support vector machines
Knowledge-Based Systems
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Hidden space support vector machines (HSSVMs) are presented in this paper. The input patterns are mapped into a high-dimensional hidden space by a set of hidden nonlinear functions and then the structural risk is introduced into the hidden space to construct HSSVMs. Moreover, the conditions for the nonlinear kernel function in HSSVMs are more relaxed, and even differentiability is not required. Compared with support vector machines (SVMs), HSSVMs can adopt more kinds of kernel functions because the positive definite property of the kernel function is not a necessary condition. The performance of HSSVMs for pattern recognition and regression estimation is also analyzed. Experiments on artificial and real-world domains confirm the feasibility and the validity of our algorithms.