[edit] Feature extraction in software
Many data analysis software systems provide for feature extraction and dimension reduction.
Feature extraction techniques
Discriminant Analysis Constructing new features via linear combination so that classification is easier.
Feature Extraction.
Adaptive Classifiers.
Visual-Based Feature Extraction and Pattern Classification.
Feature Extraction (vs. Feature Selection). The terms feature extraction and feature selection are used in the context of predictive data mining, ...
The feature extraction stage is the area at which most of the current research is directed to determine which are the relevant affective signal features to submit to the learner.
A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; ...
Neural Networks for feature extraction
One of the important problems in all of data mining is that of determining which predictors are the most relevant and the most important in building models that are most accurate at prediction.
It encloses subdisciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), ...
Clustering begins by doing feature extraction on data items and measure the values of the chosen feature set. Then the clustering model selects and compares two sets of data items and outputs the similarity measure between them.
Subsequently years, both Kirsch and I continued to pursue those same ideas - of closing in on the vision problem by combining bottom-up concepts of feature extraction with top-down theories about the syntactic and semantic structures of images.
Yoshihara, I., Kamimai, Y., and Yasunaga, M. (2001). Feature Extraction from Genome Sequence using Multi-Model Neural Network. Genome Informatics 12, 420-422.
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Digital Signal Processing generally approaches the problem of voice recognition in two steps: feature extraction followed by feature matching.
 See also: Neural network, Classification, Distribution, Dimensionality, Data mining
  
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