Automatic Pattern Recognition: A Study of the Probability of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-Scale Simulation Studies in Image Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Classifier Domains of Competence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Class Separability in Spaces Reduced By Feature Selection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A Bayesian approach to geometric subspace estimation
IEEE Transactions on Signal Processing
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of data complexity measures for classification
Expert Systems with Applications: An International Journal
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For a classification problem that is implicitly represented by a training data set, analysis of data complexity provides a linkage between context and solution. Instead of directly optimizing classification accuracy by tuning the learning algorithms, one may seek changes in the data sources and feature transformations to simplify the data geometry. Simplified class geometry benefits learning in a way common to many methods. We review some early results in data complexity analysis, compare these to recent advances in manifold learning, and suggest directions for further research.