The nature of statistical learning theory
The nature of statistical learning theory
Pairwise classification and support vector machines
Advances in kernel methods
The local paradigm for modeling and control: from neuro-fuzzy to lazy learning
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
Recursive Lazy Learning for Modeling and Control
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Leave-One-Out Support Vector Machines
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Face Pose Discrimination Using Support Vector Machines (SVM)
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Modified support vector novelty detector using training data with outliers
Pattern Recognition Letters
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
A CBR-based fuzzy decision tree approach for database classification
Expert Systems with Applications: An International Journal
Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier
Expert Systems with Applications: An International Journal
Dynamic facial expression analysis based on extended spatio-temporal histogram of oriented gradients
International Journal of Biometrics
Hi-index | 12.05 |
For training of support vector machines (SVMs) efficiently, a new training algorithm, clustering k-NN (k-nearest neighbor) support vector machines (CKSVMs) based on a Gaussian function regulated locally is proposed. In order to reflect degree of training data point as a support vector the Gaussian function is used with k-nearest neighbor (k-NN) method and Euclidean Distance measure. To add local control property to the training algorithm, a simple clustering scheme is implemented before Gaussian functions are constructed for each cluster. In addition, probabilistic SVM outputs are used for extension from binary classification to multi-class classification in pairwise approach. This training algorithm is applied to three commonly used classification problems. Experimental results show that the CKSVM has more classification accuracy than standard multi-class LS-SVM, FLS-SVM and LS-SVM with k-NN method which is proposed in our previous study. In addition to this, the training algorithm highly improved efficiency of the SVM classifier via simple algorithm.