C4.5: programs for machine learning
C4.5: programs for machine learning
Beating the hold-out: bounds for K-fold and progressive cross-validation
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Sensitive error correcting output codes
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learning compact class codes for fast inference in large multi class classification
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Efficient discriminative learning of class hierarchy for many class prediction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages
Proceedings of the 22nd international conference on World Wide Web
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We consider the problem of estimating the conditional probability of a label in time O(log n), where n is the number of possible labels. We analyze a natural reduction of this problem to a set of binary regression problems organized in a tree structure, proving a regret bound that scales with the depth of the tree. Motivated by this analysis, we propose the first online algorithm which provably constructs a logarithmic depth tree on the set of labels to solve this problem. We test the algorithm empirically, showing that it works succesfully on a dataset with roughly 106 labels.