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type of rules may be regarded as a particular form of similarity or prototype-based rules. In the voting example the similarity to the "all for it" prototype A, that is a vector with all ai = 1, has to be greater than n=2 in the Hamming distance sense, jjA¡Xjj < n=2. Cognitive psychology experiments proved that human categoriza- tion of natural objects and states of nature is based on memorization of numerous examples and creation of prototypes that are abstractions of these examples (34). Propositional logical rules are prevalent in abstract sciences but in real life they are rarely useful, their use being restricted to enumeration of small number of nominal values, or one or two continuous features with corresponding thresholds. In real life "intuitive understanding" is used more often, reflecting experience, i.e. memorized examples of patterns combined with various similarity measures that allow for their comparison and evaluation. Decision borders between different categories produced by propositional rules are simple hyperboxes. Univariate decision trees provide even simpler borders based on hierarchical reduction of decision regions to half-spaces and hyperboxes. Using similarity to prototypes quite complex decision regions may be created, including hyperboxes and fuzzy decision regions. Some of these decisions may be difficult to describe using linguistic statements and thus may server as a model of intuition. One may argue that comprehensibility of rules is lost in this way, but if similarity func-

Prototype Rules from SVM