The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
The Robustness of the p-Norm Algorithms
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Uncovering shared structures in multiclass classification
Proceedings of the 24th international conference on Machine learning
Learning a meta-level prior for feature relevance from multiple related tasks
Proceedings of the 24th international conference on Machine learning
From Aardvark to Zorro: A Benchmark for Mammal Image Classification
International Journal of Computer Vision
Generating summary keywords for emails using topics
Proceedings of the 13th international conference on Intelligent user interfaces
Efficient bandit algorithms for online multiclass prediction
Proceedings of the 25th international conference on Machine learning
Online Learning of Complex Prediction Problems Using Simultaneous Projections
The Journal of Machine Learning Research
Online learning by ellipsoid method
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Automatic Document Tagging in Social Semantic Digital Library
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Double Updating Online Learning
The Journal of Machine Learning Research
An online framework for learning novel concepts over multiple cues
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Online learning with multiple kernels: A review
Neural Computation
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We describe a general framework for online multiclass learning based on the notion of hypothesis sharing. In our framework sets of classes are associated with hypotheses. Thus, all classes within a given set share the same hypothesis. This framework includes as special cases commonly used constructions for multiclass categorization such as allocating a unique hypothesis for each class and allocating a single common hypothesis for all classes. We generalize the multiclass Perceptron to our framework and derive a unifying mistake bound analysis. Our construction naturally extends to settings where the number of classes is not known in advance but, rather, is revealed along the online learning process. We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets.