Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Journal of Machine Learning Research
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Microarray technologies are increasingly being used in biological and medical sciences for high throughput analyses of genetic information on the genome, transcriptome and proteome levels. The differentiation between cancerous and benign processes in the body often poses a difficult diagnostic problem in the clinical setting while being of major importance for the treatment of patients. In this situation, feature reduction techniques capable of reducing the dimensionality of data are essential for building predictive tools based on classification. We extend the set covering machine of Marchand and Shawe-Taylor to data dependent rays in order to achieve a feature reduction and direct interpretation of the found conjunctions of intervals on individual genes. We give bounds for the generalization error as a function of the amount of data compression and the number of training errors achieved during training. In experiments with artificial data and a real world data set of gene expression profiles from the pancreas we show the utility of the approach and its applicability to microarray data classification.