Implicitly supervised fuzzy pattern recognition

We introduce a new model of fuzzy pattern recognition where data available about class membership are given implicitly rather than explicitly. While the explicit classification training set conveys complete details about class membership, the implicit format of classification lends itself to more synthetic forms of classification outcomes (such as those expressed in terms of similarities between some pairs of patterns). The relevant architectures are proposed along with the pertinent learning schemes. >