On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Practical methods of optimization; (2nd ed.)
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Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
From on-line to batch learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Introduction to the theory of neural computation
Introduction to the theory of neural computation
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Journal of Computer and System Sciences
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
From noise-free to noise-tolerant and from on-line to batch learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Covering numbers for support vector machines
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
An introduction to support Vector Machines: and other kernel-based learning methods
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Learning in Neural Networks: Theoretical Foundations
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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
Large Margin Classification for Moving Targets
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Ultraconservative Online Algorithms for Multiclass Problems
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Learning Additive Models Online with Fast Evaluating Kernels
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
A Second-Order Perceptron Algorithm
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Single-pass online learning: performance, voting schemes and online feature selection
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Step Size Adaptation in Reproducing Kernel Hilbert Space
The Journal of Machine Learning Research
Worst-Case Analysis of Selective Sampling for Linear Classification
The Journal of Machine Learning Research
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Approximate maximum margin algorithms with rules controlled by the number of mistakes
Proceedings of the 24th international conference on Machine learning
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking the best hyperplane with a simple budget Perceptron
Machine Learning
A primal-dual perspective of online learning algorithms
Machine Learning
Fast learning of document ranking functions with the committee perceptron
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Maximal Margin Estimation with Perceptron-Like Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Online learning by ellipsoid method
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Maximum margin coresets for active and noise tolerant learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Softdoublemaxminover: perceptron-like training of support vector machines
IEEE Transactions on Neural Networks
Tighter perceptron with improved dual use of cached data for model representation and validation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Online adaptive policies for ensemble classifiers
Neurocomputing
The Journal of Machine Learning Research
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Accelerating kernel perceptron learning
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Online knowledge-based support vector machines
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Online multiple kernel learning: algorithms and mistake bounds
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
SoftDoubleMinOver: a simple procedure for maximum margin classification
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
The perceptron with dynamic margin
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Online learning meets optimization in the dual
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Tracking the best hyperplane with a simple budget perceptron
COLT'06 Proceedings of the 19th annual conference on Learning Theory
MaxMinOver regression: a simple incremental approach for support vector function approximation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Robustness analysis of eleven linear classifiers in extremely high–dimensional feature spaces
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
The huller: a simple and efficient online SVM
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
An online AUC formulation for binary classification
Pattern Recognition
A kernel fused perceptron for the online classification of large-scale data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Online Multiple Kernel Classification
Machine Learning
Online algorithm based on support vectors for orthogonal regression
Pattern Recognition Letters
The Journal of Machine Learning Research
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We describe a new incremental algorithm for training linear threshold functions: the Relaxed Online Maximum Margin Algorithm, or ROMMA. ROMMA can be viewed as an approximation to the algorithm that repeatedly chooses the hyperplane that classifies previously seen examples correctly with the maximum margin. It is known that such a maximum-margin hypothesis can be computed by minimizing the length of the weight vector subject to a number of linear constraints. ROMMA works by maintaining a relatively simple relaxation of these constraints that can be efficiently updated. We prove a mistake bound for ROMMA that is the same as that proved for the perceptron algorithm. Our analysis implies that the maximum-margin algorithm also satisfies this mistake bound; this is the first worst-case performance guarantee for this algorithm. We describe some experiments using ROMMA and a variant that updates its hypothesis more aggressively as batch algorithms to recognize handwritten digits. The computational complexity and simplicity of these algorithms is similar to that of perceptron algorithm, but their generalization is much better. We show that a batch algorithm based on aggressive ROMMA converges to the fixed threshold SVM hypothesis.