C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Pairwise classification and support vector machines
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
Multicategory Proximal Support Vector Machine Classifiers
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparallel plane proximal classifier
Signal Processing
Cancer Classification from Gene Expression Data by NPPC Ensemble
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
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In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.