C4.5: programs for machine learning
C4.5: programs for machine learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Eighteenth national conference on Artificial intelligence
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Piecewise training for structured prediction
Machine Learning
A Reformulation of Support Vector Machines for General Confidence Functions
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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We present a new approach to ensemble classification that requires learning only a single base classifier. The idea is to learn a classifier that simultaneously predicts pairs of test labels--as opposed to learning multiple predictors for single test labels-- then coordinating the assignment of individual labels by propagating beliefs on a graph over the data. We argue that the approach is statistically well motivated, even for independent identically distributed (iid) data. In fact, we present experimental results that show improvements in classification accuracy over single-example classifiers, across a range of iid data sets and over a set of base classifiers. Like boosting, the technique increases representational capacity while controlling variance through a principled form of classifier combination.