What size net gives valid generalization?
Neural Computation
Elements of information theory
Elements of information theory
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Mutual Information in Learning Feature Transformations
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Fast and Adaptive Variable Order Markov Chain Construction
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
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We propose a novel feature selection method based on a variable memory Markov (VMM) model. The VMM was originally proposed as a generative model trying to preserve the original source statistics from training data. We extend this technique to simultaneously handle several sources, and further apply a new criterion to prune out nondiscriminative features out of the model. This results in a multiclass discriminative VMM (DVMM), which is highly efficient, scaling linearly with data size. Moreover, we suggest a natural scheme to sort the remaining features based on their discriminative power with respect to the sources at hand. We demonstrate the utility of our method for text and protein classification tasks.