Machine Learning
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The genomics of a signaling pathway: a KDD Cup challenge task
ACM SIGKDD Explorations Newsletter
One class SVM for yeast regulation prediction
ACM SIGKDD Explorations Newsletter
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Classification of Anti-learnable Biological and Synthetic Data
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
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This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This has been observed so far for some natural data sets and demonstrated for some synthetic data sets when the classification rule is learned from a small set of training samples drawn from some high dimensional space. The initial analysis presented in this paper shows that anti-learning is a property of data sets and is quite distinct from over-fitting of a training data. Moreover, the analysis leads to a specification of some machine learning procedures which can overcome anti-learning and generate machines able to classify training and test data consistently.