The adoption of the object identity bias for weakening implication has lead to the definition of OI-implication, a generalization model for clausal spaces. In this paper, we investigate on the generalization hierarchy in the space ordered by OI-implication. The decidability of this relationship and the existence of minimal generalizations in the related search space is demonstrated. These results can be exploited for constructing refinement operators for incremental relational learning
Many systems that learn logic programs from examples adopt θ-subsumption as model of generalization ...
The meaning of the word generalization is so general that we can nd its occurrences in almost every ...
In this chapter, we present a polynomial time algorithm, called a k-minimal multiple generalization...
The adoption of the object identity bias for weakening implication has lead to the definition of OI-...
A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning...
Weakening implication by assuming the object identity bias allows for both a model-theoretical and a...
We present a framework for theory refinement operators ful- filling properties that ensure the effic...
In the area of inductive learning, generalization is a main operation, and the usual de nition of in...
Refinement operators for theories avoid the problems related to the myopia of many relational learni...
. The present paper discusses a generalization operator based on the -subsumption ordering between H...
Generalization is a fundamental operation of inductive inference. While first order syntactic genera...
textabstractInductive learning models [Plotkin 1971; Shapiro 1981] often use a search space of claus...
In the paper, we present some learning tasks that cannot be solved by two wellknown systems, FOIL an...
In the context of frequent pattern discovery, we present a generality relation, called thetaOI-subs...
This paper addresses methods of specialising first-order theories within the context of incremental ...
Many systems that learn logic programs from examples adopt θ-subsumption as model of generalization ...
The meaning of the word generalization is so general that we can nd its occurrences in almost every ...
In this chapter, we present a polynomial time algorithm, called a k-minimal multiple generalization...
The adoption of the object identity bias for weakening implication has lead to the definition of OI-...
A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning...
Weakening implication by assuming the object identity bias allows for both a model-theoretical and a...
We present a framework for theory refinement operators ful- filling properties that ensure the effic...
In the area of inductive learning, generalization is a main operation, and the usual de nition of in...
Refinement operators for theories avoid the problems related to the myopia of many relational learni...
. The present paper discusses a generalization operator based on the -subsumption ordering between H...
Generalization is a fundamental operation of inductive inference. While first order syntactic genera...
textabstractInductive learning models [Plotkin 1971; Shapiro 1981] often use a search space of claus...
In the paper, we present some learning tasks that cannot be solved by two wellknown systems, FOIL an...
In the context of frequent pattern discovery, we present a generality relation, called thetaOI-subs...
This paper addresses methods of specialising first-order theories within the context of incremental ...
Many systems that learn logic programs from examples adopt θ-subsumption as model of generalization ...
The meaning of the word generalization is so general that we can nd its occurrences in almost every ...
In this chapter, we present a polynomial time algorithm, called a k-minimal multiple generalization...