Additive versus exponentiated gradient updates for linear prediction
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Robust Classification for Imprecise Environments
Machine Learning
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
The lack of a priori distinctions between learning algorithms
Neural Computation
An analysis of the anti-learning phenomenon for the class symmetric polyhedron
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Non-parametric detection of meaningless distances in high dimensional data
Statistics and Computing
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We demonstrate a binary classification problem in which standard supervised learning algorithms such as linear and kernel SVM, naive Bayes, ridge regression, k-nearest neighbors, shrunken centroid, multilayer perceptron and decision trees perform in an unusual way. On certain data sets they classify a randomly sampled training subset nearly perfectly, but systematically perform worse than random guessing on cases unseen in training. We demonstrate this phenomenon in classification of a natural data set of cancer genomics microarrays using cross-validation test. Additionally, we generate a range of synthetic datasets, the outcomes of 0-sum games, for which we analyse this phenomenon in the i.i.d. setting.Furthermore, we propose and evaluate a remedy that yields promising results for classifying such data as well as normal datasets. We simply transform the classifier scores by an additional 1-dimensional linear transformation developed, for instance, to maximize classification accuracy of the outputs of an internal cross-validation on the training set. We also discuss the relevance to other fields such as learning theory, boosting, regularization, sample bias and application of kernels.