Open AccessProceedings Article
Learning Preferences for Multiclass Problems
Fabio Aiolli,Alessandro Sperduti +1 more
- 01 Dec 2004
- Vol. 17, pp 17-24
TL;DR: The Preference Learning Model is proposed as a unifying framework to model and solve a large class of multiclass problems in a large margin perspective and an original kernel-based method is proposed and evaluated on a ranking dataset with state-of-the-art results.
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Abstract: Many interesting multiclass problems can be cast in the general framework of label ranking defined on a given set of classes The evaluation for such a ranking is generally given in terms of the number of violated order constraints between classes In this paper, we propose the Preference Learning Model as a unifying framework to model and solve a large class of multiclass problems in a large margin perspective In addition, an original kernel-based method is proposed and evaluated on a ranking dataset with state-of-the-art results
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Citations
Preference learning with Gaussian processes
Wei Chu,Zoubin Ghahramani +1 more
- 07 Aug 2005
TL;DR: A probabilistic kernel approach to preference learning based on Gaussian processes and a new likelihood function is proposed to capture the preference relations in the Bayesian framework.
A neural network approach to ordinal regression
Jianlin Cheng,Zheng Wang,Gianluca Pollastri +2 more
- 01 Jun 2008
TL;DR: An effective approach to adapt a traditional neural network to learn ordinal categories is described, a generalization of the perceptron method for ordinal regression, which outperforms a neural network classification method.
Scaling large margin classifiers for spoken language understanding
TL;DR: This paper provides an original and unified presentation of these algorithms within the framework of regularized and large margin linear classifiers, reviews some available optimization techniques, and offers practical solutions to scaling issues.
48
Extensions of Gaussian processes for ranking: semi-supervised and active learning
Wei Chu,Zoubin Ghahramani +1 more
- 16 Sep 2005
TL;DR: This work focuses on ranking learning from pairwise instance preference to discuss these important extensions, semi-supervised learning and active learning, in the probabilistic framework of Gaussian processes.
Multi-task preference learning with an application to hearing aid personalization
TL;DR: An EM-algorithm for the problem of learning preferences with semiparametric models derived from Gaussian processes in the context of multi-task learning is presented and predictive results for sound quality perception of hearing-impaired subjects can be improved using a hierarchical model.
43
References
Statistical learning theory
Vladimir Vapnik
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TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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On the Learnability and Design of Output Codes for Multiclass Problems
Koby Crammer,Yoram Singer +1 more
- 01 May 2002
TL;DR: This paper discusses for the first time the problem of designing output codes for multiclass problems, and gives a time and space efficient algorithm for solving the quadratic program.
Text Categorization Based on Regularized Linear Classification Methods
Tong Zhang,Frank J. Oles +1 more
TL;DR: A number of known linear classification methods as well as some variants in the framework of regularized linear systems are compared to discuss the statistical and numerical properties of these algorithms, with a focus on text categorization.
•Proceedings Article
Log-Linear Models for Label Ranking
Ofer Dekel,Yoram Singer,Christopher D. Manning +2 more
- 09 Dec 2003
TL;DR: This work presents a general boosting-based learning algorithm for the label ranking problem and proves a lower bound on the progress of each boosting iteration.
•Journal Article
Constraint classification: A new approach to multiclass classification
TL;DR: In this article, a new view of multiclass classification and the constraint classification problem was introduced, a generalization that captures many flavors of multic-class classification, and the first optimal, distribution independent bounds for many multiclass learning algorithms, including winner-take-all (WTA), were provided.
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