Subsymbolic inductive learning framework for large-scale data processing

Recent years have witnessed the development of a large variety of Inductive methods for data analysis. This can be attributed to the fact that the decision tree-the most common representation of Inductive algorithms-provides a hierarchical framework for sequential decision making. This is a framework which non-professionals find easy to use and understand. Furthermore, it has been proved that Inductive Learning performs as well as, and indeed often better than Discriminant analysis and Multi Logic/Probit analysis. It has been also pointed out that some problems such as protein structure prediction, which are unsolvable with statistical methods can be approached quite successfully with Inductive methods. The authors aim in the paper is to express their experience in Inductive Learning in a strict form. They call this approach the subsymbolic Inductive Learning Framework, because it explores very primitive syntactic objects, and builds from them compound knowledge structures.<
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