Optimal unions of hidden classes
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The cluster analysis is a traditional tool for multi-varietal data processing. Using the k-means method, we can split a pattern set into a given number of clusters. These clusters can be used for the final classification of known output classes. This paper focuses on various approaches that can be used for an optimal union of hidden classes. The resulting tasks include binary programming or convex optimization ones. Another possibility of obtaining hidden classes is designing imperfect classifier system. Novel context out learning approach is also discussed as possibility of using simple classifiers as background of the system of hidden classes which are easy to union to output classes using the optimal algorithm. All these approaches are useful in many applications, including econometric research.
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