A simultaneous iterative method for computing projections on polyhedra
SIAM Journal on Control and Optimization
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Transition-based parsing with confidence-weighted classification
ACLstudent '10 Proceedings of the ACL 2010 Student Research Workshop
NLP on spoken documents without ASR
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Confidence in structured-prediction using confidence-weighted models
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Semantic classification of automatically acquired nouns using lexico-syntactic clues
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Selective block minimization for faster convergence of limited memory large-scale linear models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-Lingual Adaptation Using Structural Correspondence Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Distribution-aware online classifiers
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Active online classification via information maximization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
NUS at the HOO 2012 shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
A beam-search decoder for grammatical error correction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Adaptive two-view online learning for math topic classification
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Graph-Based transduction with confidence
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Adaptive regularization of weight vectors
Machine Learning
Online portfolio selection: A survey
ACM Computing Surveys (CSUR)
Multi class learning with individual sparsity
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The recently introduced online confidence-weighted (CW) learning algorithm for binary classification performs well on many binary NLP tasks. However, for multi-class problems CW learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. We derive learning algorithms for the multi-class CW setting and provide extensive evaluation using nine NLP datasets, including three derived from the recently released New York Times corpus. Our best algorithm out-performs state-of-the-art online and batch methods on eight of the nine tasks. We also show that the confidence information maintained during learning yields useful probabilistic information at test time.