Making large-scale support vector machine learning practical
Advances in kernel methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Fast Interactive 3-D Graph Visualization
GD '95 Proceedings of the Symposium on Graph Drawing
Set estimation via ellipsoidal approximations
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Theory and scope of exact representation extraction from feed-forward networks
Cognitive Systems Research
Instability of Classifiers on Categorical Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A meta-heuristic approach for improving the accuracy in some classification algorithms
Computers and Operations Research
On the role of poetic versus nonpoetic features in “kindred” and diachronic poetry attribution
Journal of the American Society for Information Science and Technology
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In this paper we present a method to extract qualitative information from any classification model that uses decision regions to generalize (e.g., feed-forward neural nets, SVMs, etc). The method's complexity is independent of the dimensionality of the input data or model, making it computationally feasible for the analysis of even very high-dimensional models. The qualitative information extracted by the method can be directly used to analyze the classification strategies employed by a model, and also to compare strategies across different model types.