C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Joint Feature Selection and Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Tracking with Adaptive Feature Selection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Extensions of vector quantization for incremental clustering
Pattern Recognition
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Feature selection for retrieval purposes
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
A new approach to perceptron training
IEEE Transactions on Neural Networks
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
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In machine vision features are the basis for almost any kind of high-level postprocessing such as classification. A new method is developed that uses the inherent flexibility of feature calculation to optimize the features for a certain classification task. By tuning the parameters of the feature calculation the accuracy of a subsequent classification can be significantly improved and the decision boundaries can be simplified. The focus of the methods is on surface inspection problems and the features and classifiers used for these applications.