Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An analysis of the max-min approach to feature selection and ordering
Pattern Recognition Letters
Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Novel Methods for Subset Selection with Respect to Problem Knowledge
IEEE Intelligent Systems
Improving Statistical Measures of Feature Subsets by Conventional and Evolutionary Approaches
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Model Complexity Validation for PDF Estimation Using Gaussian Mixtures
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
One Lead ECG Based Personal Identification with Feature Subspace Ensembles
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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The problem of feature selection in statistical pattern recognition is addressed. After formulating feature selection as a combinatorial optimisation problem, a taxonomy of approaches to feature selection is introduced. The techniques available in the literature can be logically grouped into two main categories depending on the form of density functions involved. Recent advances i the methodology of feature selection are then overviewed in this taxonomical framework. The methods discussed include the latest variants of the Branch & Bound algorithm, enhanced Floating Search techniques and the simultaneous semiparametric pfd modelling and feature space selection method.