Add to ORKGThis paper describes Zenith, a discovery system that performs constructive induction. The system is able to generate and extend new features for concept learning using agenda-based heuristic search. The search is guided by feature worth (a composite measure of discriminability and cost). Zenith is distinguished from existing constructive induction systems by its interaction with a performance system, and its ability to extend its knowledge base by creating new domain classes. Zenith is able to discover known useful features for the Othello board game
In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a...
Mich of the emphasis in current research on con-cept learning and rule Induction is based on two ass...
The system described here is concerned with the revision of inductive learning theory, i.e. the indu...
Existing methods for constructive induction usually isolate feature generation from problem solving,...
Inductive learning is an approach to machine learning in which concepts are learned from examples an...
In most concept-learning systems, users must explicitly list all features which make an example an i...
Summary. This chapter provides a short overview of a GA-based system for in-ductive concept learning...
178 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998.HCL achieves two functionalit...
One subfield of machine learning is the induction of a representation of a concept from positive and...
While similarity-based learning (SBL) methods can be effective for acquiring concept descriptions fr...
This paper describes a framework that generates constructive induction schemes for the concept forma...
The available concept-learners only partially fulfill the needs imposed by the learning apprentice g...
Summarization: Post and prior to learning concept perception may vary. Inductive learning systems su...
This thesis presents a careful experimental examination of the task of concept formation. This learn...
LAIR is a system that incrementally learns conjunctive concept descriptions from positive and negati...
In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a...
Mich of the emphasis in current research on con-cept learning and rule Induction is based on two ass...
The system described here is concerned with the revision of inductive learning theory, i.e. the indu...
Existing methods for constructive induction usually isolate feature generation from problem solving,...
Inductive learning is an approach to machine learning in which concepts are learned from examples an...
In most concept-learning systems, users must explicitly list all features which make an example an i...
Summary. This chapter provides a short overview of a GA-based system for in-ductive concept learning...
178 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998.HCL achieves two functionalit...
One subfield of machine learning is the induction of a representation of a concept from positive and...
While similarity-based learning (SBL) methods can be effective for acquiring concept descriptions fr...
This paper describes a framework that generates constructive induction schemes for the concept forma...
The available concept-learners only partially fulfill the needs imposed by the learning apprentice g...
Summarization: Post and prior to learning concept perception may vary. Inductive learning systems su...
This thesis presents a careful experimental examination of the task of concept formation. This learn...
LAIR is a system that incrementally learns conjunctive concept descriptions from positive and negati...
In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a...
Mich of the emphasis in current research on con-cept learning and rule Induction is based on two ass...
The system described here is concerned with the revision of inductive learning theory, i.e. the indu...