Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: Proceedings of the Ninth International Conference on Artificial Neural Networks, Edinburgh, UK (1999) 97--102 Home/Search Document Details and Download
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The Fidelity of Local Ordinal Encoding - Sadr, al. (2001) (Correct)
....I(p a ) I(p a1 ) I(p a2 ) I(p a5 ) 1) Figure 4: Ordinal relationships between an image region p a and its neighbors. retrieval results [5]. Our regularization term is a norm in a Reproducing Kernel Hilbert Space (RKHS) 2, 11] Minimizing the norm in a RKHS subject to the ordinal constraints corresponds to the following convex constrained quadratic optimization problem: min #,w 2 w C # p (2) #(# p )w (#(x pa ) # ....
R. Herbrich, T. Graepel, and K. Obermeyer. Support vector learning for ordinal regression. In Proc. of the Ninth Intl. Conf. on Artificial Neural Networks, pages 97--102, 1999.
Prediction of Ordinal Classes Using Regression Trees - Kramer, Widmer, al. (2000) (3 citations) (Correct)
....made should be consistent with the order of the attribute values in the decision nodes. The authors present repair strategies for correcting inconsistent trees in case these consistency constraints are violated, as well as an algorithm for constructing consistent trees in the rst place. [Herbrich et al. 1999, Herbrich et al. 1999b] describe an algorithm based on the large margin idea known from data dependent Structural Risk Minimization [Shawe Taylor et al. 1996] The algorithm is similar to Support Vector Machines [Cortes Vapnik, 1995] They demonstrate good results on arti cial data and on a ....
....with the order of the attribute values in the decision nodes. The authors present repair strategies for correcting inconsistent trees in case these consistency constraints are violated, as well as an algorithm for constructing consistent trees in the rst place. Herbrich et al. 1999, Herbrich et al. 1999b] describe an algorithm based on the large margin idea known from data dependent Structural Risk Minimization [Shawe Taylor et al. 1996] The algorithm is similar to Support Vector Machines [Cortes Vapnik, 1995] They demonstrate good results on arti cial data and on a (very small) ....
Herbrich, R., Graepel, T., and Obermayer, K. (1999a). Support Vector Learning for Ordinal Regression. In Proceedings of the Ninth International Conference on Articial Neural Networks, pp.97-102.
Analyzing Sensory Data Using Non-Linear - Preference Learning With (2004) (Correct)
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Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: Proceedings of the Ninth International Conference on Artificial Neural Networks, Edinburgh, UK (1999) 97--102
AUC Maximizing Support Vector Learning - Brefeld, Scheffer (2005) (Correct)
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Herbrich, R., Graepel, T., & Obermayer, K. (1999). Support vector learning for ordinal regression. Proceedings of the International Conference on Artificial Neural Networks. Herschtal, A., & Raskutti, B. (2004). Optimizing the area under curve. Proceedings of the International Conference on Machine Learning.
Prediction of Ordinal Classes Using Regression Trees - Kramer, al. (2001) (3 citations) (Correct)
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Herbrich, R., Graepel, T., Obermayer, K.: Support Vector Learning for Ordinal Regression. In Proceedings of the Ninth International Conference on Artificial Neural Networks, 1999, 97--102.
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