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http://www.gatsby.ucl.ac.uk/%7Echuwei/paper/icmlsvor.pdf
New Approaches to Support Vector Ordinal Regression the thresholds, exactly as Shashua and Levin (2003) proposed, but we introduce explicit constraints in the problem formulation that enforce the inequalities on the thresholds. The second approach is entirely new; it considers the training samples from all the ranks to determine each threshold.
https://dl.acm.org/citation.cfm?id=1102370
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.Cited by: 321
https://www.researchgate.net/publication/221346030_New_approaches_to_support_vector_ordinal_regression
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.1761
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.
http://www.gatsby.ucl.ac.uk/~chuwei/paper/svor.pdf
Support Vector Ordinal Regression Wei Chu [email protected] Center for Computational Learning Systems, Columbia University, New York, NY 10115 USA S. Sathiya Keerthi [email protected] Yahoo! Research, Media Studios North, Burbank, CA 91504 USA Abstract In this paper, we propose two new support vector approaches for ordinal regression,
https://www.researchgate.net/publication/6507614_Support_Vector_Ordinal_Regression
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales.
http://keerthis.com/ord_nc_chu_06.pdf
Support Vector Ordinal Regression Wei Chu [email protected] Center for Computational Learning Systems, Columbia University, New York, NY 10115 USA S. Sathiya Keerthi [email protected] Yahoo! Research, Media Studios North, Burbank, CA 91504 USA Abstract In this paper, we propose two new support vector approaches for ordinal regression,
https://www.semanticscholar.org/paper/Support-Vector-Ordinal-Regression-Chu-Keerthi/de1729ca1d9dc951976f1f9fe2a08184207263a7
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal ...
https://www.ncbi.nlm.nih.gov/pubmed/17298234
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.Cited by: 269
https://www.mitpressjournals.org/doi/10.1162/neco.2007.19.3.792
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these ...Cited by: 269
http://www.gatsby.ucl.ac.uk/%7Echuwei/paper/icmlsvor.pdf
New Approaches to Support Vector Ordinal Regression the thresholds, exactly as Shashua and Levin (2003) proposed, but we introduce explicit constraints in the problem formulation that enforce the inequalities on the thresholds. The second approach is entirely new; it considers the training samples from all the ranks to determine each threshold.
https://dl.acm.org/citation.cfm?id=1102370
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.Cited by: 321
https://www.researchgate.net/publication/221346030_New_approaches_to_support_vector_ordinal_regression
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.1761
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.
http://www.gatsby.ucl.ac.uk/~chuwei/paper/svor.pdf
Support Vector Ordinal Regression Wei Chu [email protected] Center for Computational Learning Systems, Columbia University, New York, NY 10115 USA S. Sathiya Keerthi [email protected] Yahoo! Research, Media Studios North, Burbank, CA 91504 USA Abstract In this paper, we propose two new support vector approaches for ordinal regression,
https://www.researchgate.net/publication/6507614_Support_Vector_Ordinal_Regression
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales.
http://keerthis.com/ord_nc_chu_06.pdf
Support Vector Ordinal Regression Wei Chu [email protected] Center for Computational Learning Systems, Columbia University, New York, NY 10115 USA S. Sathiya Keerthi [email protected] Yahoo! Research, Media Studios North, Burbank, CA 91504 USA Abstract In this paper, we propose two new support vector approaches for ordinal regression,
https://www.semanticscholar.org/paper/Support-Vector-Ordinal-Regression-Chu-Keerthi/de1729ca1d9dc951976f1f9fe2a08184207263a7
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal ...
https://www.ncbi.nlm.nih.gov/pubmed/17298234
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.Cited by: 269
https://www.mitpressjournals.org/doi/10.1162/neco.2007.19.3.792
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these ...Cited by: 269
http://www.gatsby.ucl.ac.uk/~chuwei/paper/svor.pdf
Shashua and Levin (2003) generalized the support vector formulation for ordinal regression by finding r−1 thresholds that divide the real line into r consecutive intervals for the r ordered categories. However there is a problem with their approach: the ordinal inequalities on the thresholds, b 1 ≤ b
https://www.semanticscholar.org/paper/Support-Vector-Ordinal-Regression-Chu-Keerthi/de1729ca1d9dc951976f1f9fe2a08184207263a7
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal ...
https://core.ac.uk/display/20742662
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.
https://digital-library.theiet.org/content/conferences/10.1049/cp_19991091
Similar to support vector methods we derive a new learning algorithm for the task of ordinal regression based on large margin rank boundaries. We give experimental results for an information retrieval task: learning the order of documents with respect to an initial query.
https://www.mitpressjournals.org/doi/10.1162/neco.2007.19.3.792
In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these ...
http://papers.nips.cc/paper/3125-ordinal-regression-by-extended-binary-classification.pdf
binary classification approaches, but also to derive new generalization bounds for ordinal regression from known bounds for binary classification. In addition, our framework unifies many existing ordinal regression algorithms, such as percep-tron ranking and support vector ordinal regression. When compared empirically
http://orca.st.usm.edu/~zwang/files/rank.pdf
a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classificatio n method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank
https://www.groundai.com/project/rank-consistent-logits-for-ordinal-regression-with-convolutional-neural-networks/1
While extraordinary progress has been made towards developing neural network architectures for classification tasks, commonly used loss functions such as the multi-category cross entropy loss are inadequate for ranking and ordinal regression problems. To address this issue, approaches have been developed that transform ordinal target variables series of binary classification tasks, resulting ...
http://www.kbs.twi.tudelft.nl/docs/MSc/2005/Dikkers_H_J/thesis.pdf
drawbacks in both its mathematical fundamentals and its implementation. We propose a new credit rating framework, which incorporates an improved version of the current model. Aside from this expert system, we applied linear regression, ordinal logistic regression, and support vector machine techniques to the credit rating problem.
https://link.springer.com/chapter/10.1007/978-3-030-29911-8_13
Most existing ordinal regression methods are adapted from traditional supervised learning algorithms (e.g., support vector machines and neural networks) which have shown to work well mostly on dense data. However, the use of existing ordinal regression methods on …
https://www.researchgate.net/publication/220372358_Leave-one-out_bounds_for_support_vector_ordinal_regression_machine
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales.
https://www.sciencedirect.com/science/article/abs/pii/S0893608017301090
In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to more intuitive parameter update rules.
https://www.sciencedirect.com/science/article/pii/S1877050917306567
However, few studies focused on linear SVM-based ordinal regression models. In this paper, we propose a new approach, called linear Nonparallel Support Vector Ordinal Regression (NPSVOR), which can deal with large-scale problems.
https://link.springer.com/chapter/10.1007/978-3-642-40994-3_7
Abstract. We show that classification rules used in ordinal regression are equivalent to a certain class of linear multi-class classifiers. This observation not only allows to design new learning algorithms for ordinal regression using existing methods for multi-class classification but it also allows to derive new models for ordinal regression.
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