Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
Elements of information theory
Elements of information theory
The nature of statistical learning theory
The nature of statistical learning theory
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
The Journal of Machine Learning Research
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalization Bounds for Some Ordinal Regression Algorithms
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
Large-Margin thresholded ensembles for ordinal regression: theory and practice
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
An experimental study of different ordinal regression methods and measures
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Neural network ensembles to determine growth multi-classes in predictive microbiology
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
Exploitation of pairwise class distances for ordinal classification
Neural Computation
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We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the EachMovie data set for collaborative filtering. In the experiments we performed, our algorithms outperform online algorithms for regression and classification applied to ranking.