Abstract: For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach in order to make a solver quickly. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. It is noted that our formal modeling language is not intend for providing an efficient notation for data mining application, but for facilitating a designer who develops solvers in machine learning.
Abstract: For a given specific problem an efficient algorithm has
been the matter of study. However, an alternative approach orthogonal
to this approach comes out, which is called a reduction. In general
for a given specific problem this reduction approach studies how to
convert an original problem into subproblems. This paper proposes
a formal modeling language to support this reduction approach. We
show three examples from the wide area of learning problems. The
benefit is a fast prototyping of algorithms for a given new problem.