A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Mean field methods for classification with Gaussian processes
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Efficient computations for large least square support vector machine classifiers
Pattern Recognition Letters
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Gradient-Based Optimization of Hyperparameters
Neural Computation
Optimizing resources in model selection for support vector machine
Pattern Recognition
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
Pruning error minimization in least squares support vector machines
IEEE Transactions on Neural Networks
An improved conjugate gradient scheme to the solution of least squares SVM
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Computers and Electronics in Agriculture
Help-Training for semi-supervised support vector machines
Pattern Recognition
Towards non invasive diagnosis of scoliosis using semi-supervised learning approach
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Robust twin support vector machine for pattern classification
Pattern Recognition
Artificial Intelligence in Medicine
Structural twin support vector machine for classification
Knowledge-Based Systems
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Using robust dispersion estimation in support vector machines
Pattern Recognition
A tensor factorization based least squares support tensor machine for classification
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Efficient sparse least squares support vector machines for pattern classification
Computers & Mathematics with Applications
Computers and Electronics in Agriculture
Hi-index | 0.02 |
The support vector machine (SVM) is a powerful classifier which has been used successfully in many pattern recognition problems. It has also been shown to perform well in the handwriting recognition field. The least squares SVM (LS-SVM), like the SVM, is based on the margin-maximization principle performing structural risk minimization. However, it is easier to train than the SVM, as it requires only the solution to a convex linear problem, and not a quadratic problem as in the SVM. In this paper, we propose to conduct model selection for the LS-SVM using an empirical error criterion. Experiments on handwritten character recognition show the usefulness of this classifier and demonstrate that model selection improves the generalization performance of the LS-SVM.