Making large-scale support vector machine learning practical
Advances in kernel methods
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Relaxed Online Maximum Margin Algorithm
Machine Learning
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate maximum margin algorithms with rules controlled by the number of mistakes
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Constant rate approximate maximum margin algorithms
ECML'06 Proceedings of the 17th European conference on Machine Learning
Analysis of generic perceptron-like large margin classifiers
ECML'05 Proceedings of the 16th European conference on Machine Learning
The Margitron: A Generalized Perceptron With Margin
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum margin classifier called the perceptron with dynamic margin (PDM). We demonstrate that PDM converges in a finite number of steps and derive an upper bound on them. We also compare experimentally PDM with other perceptron-like algorithms and support vector machines on hard margin tasks involving linear kernels which are equivalent to 2-norm soft margin.