OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Generalization Bounds for Some Ordinal Regression Algorithms
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Preferential text classification: learning algorithms and evaluation measures
Information Retrieval
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Block-quantized support vector ordinal regression
IEEE Transactions on Neural Networks
Preference learning with extreme examples
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Customer churn time prediction in mobile telecommunication industry using ordinal regression
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A transitivity analysis of bipartite rankings in pairwise multi-class classification
Information Sciences: an International Journal
Ordinal extreme learning machine
Neurocomputing
Search behavior-driven training for result re-ranking
TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
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
Prototype based modelling for ordinal classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Adaptive metric learning vector quantization for ordinal classification
Neural Computation
Evolutionary extreme learning machine for ordinal regression
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Exploitation of pairwise class distances for ordinal classification
Neural Computation
Kernelizing the proportional odds model through the empirical kernel mapping
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
An n-spheres based synthetic data generator for supervised classification
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Can machine learning techniques help to improve the common fisheries policy?
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
An organ allocation system for liver transplantation based on ordinal regression
Applied Soft Computing
Feature selection for ordinal text classification
Neural Computation
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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 optimization algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.