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.
Keywords
Margin (machine learning)Decision boundaryGeneralizationPerceptronComputer scienceAlgorithmDimension (graph theory)Artificial intelligenceBoundary (topology)Variety (cybernetics)Pattern recognition (psychology)Machine learningMathematicsArtificial neural networkSupport vector machine
Affiliated Institutions
Related Publications
Backpropagation training for multilayer conditional random field based phone recognition
Conditional random fields (CRFs) have recently found increased popularity in automatic speech recognition (ASR) applications. CRFs have previously been shown to be effective com...
Boosting attribute and phone estimation accuracies with deep neural networks for detection-based speech recognition
Generation of high-precision sub-phonetic attribute (also known as phonological features) and phone lattices is a key frontend component for detection-based bottom-up speech rec...
Object Detection with Discriminatively Trained Part-Based Models
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-...
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has b...
Publication Info
- Year
- 1992
- Type
- article
- Pages
- 144-152
- Citations
- 11404
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
11404
OpenAlex
Cite This
Bernhard E. Boser,
Isabelle Guyon,
Vladimir Vapnik
(1992).
A training algorithm for optimal margin classifiers.
, 144-152.
https://doi.org/10.1145/130385.130401
Identifiers
- DOI
- 10.1145/130385.130401