Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Soft learning vector quantization
Neural Computation
Efficient Clustering for Orders
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Label ranking by learning pairwise preferences
Artificial Intelligence
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Decision tree and instance-based learning for label ranking
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Journal of Artificial Intelligence Research
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Regression Learning Vector Quantization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Robust reductions from ranking to classification
COLT'07 Proceedings of the 20th annual conference on Learning theory
The Journal of Machine Learning Research
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
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We propose a multi-prototype-based algorithm for online learning of soft pairwise-preferences over labels. The algorithm learns soft label preferences via minimization of the proposed soft rank-loss measure, and can learn from total orders as well as from various types of partial orders. The soft pairwise preference algorithm outputs are further aggregated to produce a total label ranking prediction using a novel aggregation algorithm that outperforms existing aggregation solutions. Experiments on synthetic and real-world data demonstrate state-of-the-art performance of the proposed model.