A training algorithm for optimal margin classifiers¶
Why this mattered¶
TBD
Abstract¶
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
Related¶
- enables → Gradient-based learning applied to document recognition — Optimal-margin classifiers influenced LeNet's comparison to margin-based supervised learning for document recognition.
- cite ← Support-Vector Networks — Support-vector networks develop the optimal-margin classifier framework introduced by Boser, Guyon, and Vapnik into a broader kernel-based method.
- cite ← Gradient-based learning applied to document recognition — LeCun's document-recognition work contrasts convolutional neural networks with optimal-margin classifiers as competing supervised methods for handwritten digit recognition.
- cite ← Support-vector networks — Support-vector networks develop the optimal-margin classifier idea from Boser, Guyon, and Vapnik into the broader support-vector machine framework with kernels.