Communications of the ACM
From on-line to batch learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
Making large-scale support vector machine learning practical
Advances in kernel methods
On PAC learning using Winnow, Perceptron, and a Perceptron-like algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
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
Machine Learning
The Relaxed Online Maximum Margin Algorithm
Machine Learning
The Perceptron Algorithm with Uneven Margins
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Selective Voting for Perception-like Online Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
A Second-Order Perceptron Algorithm
SIAM Journal on Computing
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Analysis of generic perceptron-like large margin classifiers
ECML'05 Proceedings of the 16th European conference on Machine Learning
A new perspective on an old perceptron algorithm
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IEEE Transactions on Signal Processing
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
Learning from interpretations: a rooted kernel for ordered hypergraphs
Proceedings of the 24th international conference on Machine learning
Label ranking by learning pairwise preferences
Artificial Intelligence
Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Learning Similarity Functions from Qualitative Feedback
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Suppressing outliers in pairwise preference ranking
Proceedings of the 17th ACM conference on Information and knowledge management
Decision tree and instance-based learning for label ranking
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A team of continuous-action learning automata for noise-tolerant learning of half-spaces
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning action effects in partially observable domains
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Example-dependent basis vector selection for kernel-based classifiers
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Efficient multilabel classification algorithms for large-scale problems in the legal domain
Semantic Processing of Legal Texts
Modeling information exchange opportunities for effective human-computer teamwork
Artificial Intelligence
Adaptive regularization of weight vectors
Machine Learning
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A large number of variants of the Perceptron algorithm have been proposed and partially evaluated in recent work. One type of algorithm aims for noise tolerance by replacing the last hypothesis of the perceptron with another hypothesis or a vote among hypotheses. Another type simply adds a margin term to the perceptron in order to increase robustness and accuracy, as done in support vector machines. A third type borrows further from support vector machines and constrains the update function of the perceptron in ways that mimic soft-margin techniques. The performance of these algorithms, and the potential for combining different techniques, has not been studied in depth. This paper provides such an experimental study and reveals some interesting facts about the algorithms. In particular the perceptron with margin is an effective method for tolerating noise and stabilizing the algorithm. This is surprising since the margin in itself is not designed or used for noise tolerance, and there are no known guarantees for such performance. In most cases, similar performance is obtained by the voted-perceptron which has the advantage that it does not require parameter selection. Techniques using soft margin ideas are run-time intensive and do not give additional performance benefits. The results also highlight the difficulty with automatic parameter selection which is required with some of these variants.