The perception: a probabilistic model for information storage and organization in the brain
Neurocomputing: foundations of research
The weighted majority algorithm
Information and Computation
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Journal of the ACM (JACM)
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
The Robustness of the p-Norm Algorithms
Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
A Second-Order Perceptron Algorithm
SIAM Journal on Computing
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Efficient algorithms for online convex optimization and their applications
Efficient algorithms for online convex optimization and their applications
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Multi-class confidence weighted algorithms
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
Large Scale Online Learning of Image Similarity Through Ranking
The Journal of Machine Learning Research
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Virtual vector machine for Bayesian online classification
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Adapting bias by gradient descent: an incremental version of delta-bar-delta
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Confidence in structured-prediction using confidence-weighted models
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Confidence-weighted learning of factored discriminative language models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
The Journal of Machine Learning Research
Re-adapting the regularization of weights for non-stationary regression
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
The huller: a simple and efficient online SVM
ECML'05 Proceedings of the 16th European conference on Machine Learning
An online framework for learning novel concepts over multiple cues
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Confidence-weighted linear classification for text categorization
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We present AROW, an online learning algorithm for binary and multiclass problems that combines large margin training, confidence weighting, and the capacity to handle non-separable data. AROW performs adaptive regularization of the prediction function upon seeing each new instance, allowing it to perform especially well in the presence of label noise. We derive mistake bounds for the binary and multiclass settings that are similar in form to the second order perceptron bound. Our bounds do not assume separability. We also relate our algorithm to recent confidence-weighted online learning techniques. Empirical evaluations show that AROW achieves state-of-the-art performance on a wide range of binary and multiclass tasks, as well as robustness in the face of non-separable data.