Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The nature of statistical learning theory
The nature of statistical learning theory
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning What People (Don't) Want
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Polycategorical classification deals with the task of solving multiple interdependent classification problems. The key challenge is to systematically exploit possible dependencies among the labels to improve on the standard approach of solving each classification problem independently. Our method operates in two stages: the first stage uses the observed set of labels to learn a joint label model that can be used to predict unobserved pattern labels purely based on inter-label dependencies. The second stage uses the observed labels as well as inferred label predictions as input to a generalized transductive support vector machine. The resulting mixed integer program is heuristically solved with a continuation method. We report experimental results on a collaborative filtering task that provide empirical support for our approach.