# Graph-based generation of a meta-learning search space

International Journal of Applied Mathematics and Computer Science (2012)

- Volume: 22, Issue: 3, page 647-667
- ISSN: 1641-876X

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topNorbert Jankowski. "Graph-based generation of a meta-learning search space." International Journal of Applied Mathematics and Computer Science 22.3 (2012): 647-667. <http://eudml.org/doc/244065>.

@article{NorbertJankowski2012,

abstract = {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. 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. 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.},

author = {Norbert Jankowski},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {meta-learning; data mining; learning machines; complexity of learning; complexity of learning machines; computational intelligence},

language = {eng},

number = {3},

pages = {647-667},

title = {Graph-based generation of a meta-learning search space},

url = {http://eudml.org/doc/244065},

volume = {22},

year = {2012},

}

TY - JOUR

AU - Norbert Jankowski

TI - Graph-based generation of a meta-learning search space

JO - International Journal of Applied Mathematics and Computer Science

PY - 2012

VL - 22

IS - 3

SP - 647

EP - 667

AB - 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. 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. 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.

LA - eng

KW - meta-learning; data mining; learning machines; complexity of learning; complexity of learning machines; computational intelligence

UR - http://eudml.org/doc/244065

ER -

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