Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Sphere-structured support vector machines for multi-class pattern recognition
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Structured One-Class Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning pattern classification-a survey
IEEE Transactions on Information Theory
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A DIAMOND method of inducing classification rules for biological data
Computers in Biology and Medicine
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This paper presents a kernel-based fuzzy greedy multiple hyperspheres covering algorithm for pattern classification. In the training process all training data of each class are covered by multiple hyperspheres constructed, each of which encompasses as many data as possible via a greedy method. In the classification process a fuzzy membership function is defined to label the testing samples. Furthermore, we introduce kernel methods into the proposed method. To investigate the effectiveness of our approach, experiments are done on artificial data sets and six real data sets. Experimental results show that our algorithm not only can acquire the lower time complexity in training and the better classification accuracies than two hyperspheres-based classification methods, but also can achieve the comparable performance to the classical support vector machines.