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Download e-book for iPad: Algorithms and Theory in Filtering and Control, part 1 by Sorensen D.C., Wets R.J.-B. (eds.)

By Sorensen D.C., Wets R.J.-B. (eds.)

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Additional resources for Algorithms and Theory in Filtering and Control, part 1

Example text

It not only queries for the error points (or points having low margin) but also a number of other points far from the separating hyperplane (interior points). Thus, even if a current hypothesis is erroneous there is a scope for its being corrected owing to the interior points. If only error points were selected the hypothesis might have actually been worse. The ratio of selected points having low margin and those far from the hyperplane is decided by the confidence factor, which varies adaptively with iteration.

An advanced condensation algorithm should include information from all samples in the reduction process. Some widely studied schemes for data condensation are built upon classification-based approaches, in general, and the k-NN rule, in particular [48]. The effectiveness of the condensed set is measured in terms of the classification accuracy. , a minimal set which correctly classifies all the original samples. The very first development of this kind is the condensed nearest neighbor rule (CNN) of Hart [91].

Some representative groups are mentioned below: 1. , linear discriminants [59, 92]) 2. , Bayesian networks [102]) 3. , CART [32]) 4. , PROGOL [180] and FOIL [233]) 5. , nearest neighbor [7], lazy learning [5] and case based reasoning [122, 208] methods ) 6. Neural network based models [44, 46, 148, 266] 7. Fuzzy set theoretic models [16, 23, 43, 217] 8. Rough set theory based models [137, 123, 227, 176] 9. Genetic algorithm based models [68, 106] 10. Hybrid and soft computing models [175] The data mining algorithms determine both the flexibility of the model in representing the data and the interpretability of the model in human terms.

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