The perception: a probabilistic model for information storage and organization in the brain
Neurocomputing: foundations of research
The nature of statistical learning theory
The nature of statistical learning theory
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Smoothed analysis of the perceptron algorithm for linear programming
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
The Relaxed Online Maximum Margin Algorithm
Machine Learning
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Step Size Adaptation in Reproducing Kernel Hilbert Space
The Journal of Machine Learning Research
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
The projectron: a bounded kernel-based Perceptron
Proceedings of the 25th international conference on Machine learning
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
Coresets for polytope distance
Proceedings of the twenty-fifth annual symposium on Computational geometry
A simpler unified analysis of budget perceptrons
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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We present a generalization of the Perceptron algorithm. The new algorithm performs a Perceptron-style update whenever the margin of an example is smaller than a predefined value. We derive worst case mistake bounds for our algorithm. As a byproduct we obtain a new mistake bound for the Perceptron algorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which we compare the proposed algorithm to the Perceptron algorithm.