Making large-scale support vector machine learning practical
Advances in kernel methods
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Collaborative ordinal regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The Journal of Machine Learning Research
A machine learning approach to TCP throughput prediction
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Semi-supervised learning with data calibration for long-term time series forecasting
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Via Local Regression
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Clustering with Feature Order Preferences
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Semi-Supervised Multi-Task Regression
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Clustering with local and global regularization
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A general learning framework using local and global regularization
Pattern Recognition
Clustering with feature order preferences
Intelligent Data Analysis - Artificial Intelligence
How about utilizing ordinal information from the distribution of unlabeled data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
RankSVR: can preference data help regression?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Ordinal extreme learning machine
Neurocomputing
Semi-supervised Elastic net for pedestrian counting
Pattern Recognition
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We propose a novel kernel regression algorithm which takes into account order preferences on unlabeled data. Such preferences have the form that point x1 has a larger target value than that of x2, although the target values for x1, x2 are unknown. The order preferences can be viewed as side information or a form of weak labels, and our algorithm can be related to semi-supervised learning. Learning consists of formulating the order preferences as additional regularization in a risk minimization framework. We define a linear program to effectively solve the optimization problem. Experiments on benchmark datasets, sentiment analysis, and housing price problems show that the proposed algorithm outperforms standard regression, even when the order preferences are noisy.