S. Har Peled, D. Roth, and D. Zimak. Constraint classification: A new approach to multiclass classification. In Proceedings of the 13th International Conference on Algorithmic Learning Theory (ALT-02), 2002. Home/Search Document Not in Database Summary Related Articles Check |
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Learning with Equivalence Constraints, and the Relation to .. - Bar-Hillel, Weinshall (2003) (Correct)
....functions. Support vector machines (SVM) 14] and boosting techniques [13] are two important examples. A possible solution is to reduce the problem to the learning of a number of binary classifiers (O(M) or O(M ) and then combine the classifiers using for example a winnertakes all strategy [7]. The use of error correcting code to combine the binary classifiers was first suggested in [5] Such codes were used in several successful generalizations to existing techniques, such as multiclass SVM and multiclass boosting [6, 1] These solutions are hard to analyze, however, and only recently ....
.... codes were used in several successful generalizations to existing techniques, such as multiclass SVM and multiclass boosting [6, 1] These solutions are hard to analyze, however, and only recently have we started to understand the properties of these algorithms, such as their sample complexity [7]. Another possible solution is to assume that the data distribution is known and construct a generative model, e.g. a Gaussian mixture model. The main drawback of this approach is the strong dependence on the assuption that the distribution is known. In this paper we propose a di#erent approach ....
S. Har-Peled, D. Roth, D. Zimak. Constraints classification:A new approach to multiclass classification and ranking. In Proc. NIPS, 2002.
Learning Preferences for Multiclass Problems - Fabio Aiolli Dept (Correct)
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S. Har Peled, D. Roth, and D. Zimak. Constraint classification: A new approach to multiclass classification. In Proceedings of the 13th International Conference on Algorithmic Learning Theory (ALT-02), 2002.
Active Learning of Label Ranking Functions - Klaus Brinker Kbrinker (Correct)
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Har-Peled, S., Roth, D., & Zimak, D. (2002). Constraint classification: A new approach to multiclass classification and ranking. Advances in Neural Information Processing Systems 15 (NIPS).
Gaussian Processes for Ordinal Regression - Chu, Ghahramani (2005) (1 citation) (Correct)
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S. Har-Peled, D. Roth, and D. Zimak. Constraint classification: A new approach to multiclass classification and ranking. In S. Thrun S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 785--792, 2003.
On Position Error and Label Ranking through Iterated Choice - Hüllermeier, Fürnkranz.. (Correct)
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S. Har-Peled, D. Roth, and D. Zimak. Constraint classification: a new approach to multiclass classification. In Proc. ALT-02, pp. 365--379, L ubeck, 2002.
Learning Preferences for Multiclass Problems - Aiolli, Sperduti (2004) (1 citation) (Correct)
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S. Har Peled, D. Roth, and D. Zimak. Constraint classification: A new approach to multiclass classification. In Proceedings of the 13th International Conference on Algorithmic Learning Theory (ALT-02), 2002.
Adapting Codes and Embeddings for Polychotomies - Rätsch, Smola, Mika (Correct)
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S. Har-Peled, D. Roth, and D. Zimak. Constraint classification: A new approach to multiclass classification and ranking. In NIPS, vol. 15. MIT Press, 2003.
Instance-Based Prediction with Guaranteed Confidence - Hüllermeier (2004) (Correct)
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S. Har-Peled, D. Roth, and D. Zimak, `Constraint classification: a new approach to multiclass classification', in Proceedings 13th Int. Conf. on Algorithmic Learning Theory, pp. 365--379, Lubeck, Germany, (2002).
Ranking by Pairwise Comparison: A Note on Risk Minimization - Hüllermeier, Fürnkranz (Correct)
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S. Har-Peled, D. Roth, and D. Zimak. Constraint classification: a new approach to multiclass classification. In Proceedings 13th Int. Conf. on Algorithmic Learning Theory, pages 365--379, Lubeck, Germany, 2002.
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