Elements of information theory
Elements of information theory
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Linear hinge loss and average margin
Proceedings of the 1998 conference on Advances in neural information processing systems II
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Regret-based online ranking for a growing digital library
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Double Updating Online Learning
The Journal of Machine Learning Research
| Hi-index | 0.02 |
Category ranking is the task of ordering labels with respect to their relevance to an input instance. In this paper we describe and analyze several algorithms for online category ranking where the instances are revealed in a sequential manner. We describe additive and multiplicative updates which constitute the core of the learning algorithms. The updates are derived by casting a constrained optimization problem for each new instance. We derive loss bounds for the algorithms by using the properties of the dual solution while imposing additional constraints on the dual form. Finally, we outline and analyze the convergence of a general update that can be employed with any Bregman divergence.