TY - GEN A1 - Jankowski, Norbert A2 - Korbicz, Józef - red. A2 - Uciński, Dariusz - red. PB - Zielona Góra: Uniwersytet Zielonogórski N2 - Meta-learning is becoming more and more important in current and future research concentrated around broadly defined data mining or computational intelligence. It can solve problems that cannot be solved by any single, specialized algorithm. The overall characteristic of each meta-learning algorithm mainly depends on two elements: the learning machine space and the supervisory procedure. N2 - The former restricts the space of all possible learning machines to a subspace to be browsed by a meta-learning algorithm. The latter determines the order of selected learning machines with a module responsible for machine complexity "evaluation", organizes tests and performs analysis of results. In this article we present a framework for meta-learning search that can be seen as a method of sophisticated description and evaluation of functional search spaces of learning machine configurations used in meta-learning N2 - Machine spaces will be defined by specially defined graphs where vertices are specialized machine configuration generators. By using such graphs the learning machine space may be modeled in a much more flexible way, depending on the characteristics of the problem considered and "a priori" knowledge. The presented method of search space description is used together with an advanced algorithm which orders test tasks according to their complexities. L1 - http://zbc.uz.zgora.pl/Content/47016/AMCS_2012_22_3_12.pdf L2 - http://zbc.uz.zgora.pl/Content/47016 KW - meta-learning KW - data mining KW - learning machines KW - complexity of learning KW - complexity of learning machines KW - computational intelligence T1 - Graph-based generation of a meta-learning search space UR - http://zbc.uz.zgora.pl/dlibra/docmetadata?id=47016 ER -