Book ChapterDOI
Guiding induction with domain theories
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In this paper, the authors present a concept-acquisition methodology that uses data (concept examples and counterexamples), domain knowledge, and tentative concept descriptions in an integrated way.Abstract:
In this chapter we present a concept-acquisition methodology that uses data (concept examples and counterexamples), domain knowledge, and tentative concept descriptions in an integrated way. Domain knowledge can be incomplete and/or incorrect with respect to the given data; moreover, the tentative concept descriptions can be expressed in a form that is not operational. The methodology is aimed at producing discriminant and operational concept descriptions, by integrating inductive and deductive learning. In fact, the domain theory is used in a deductive process, that tries to operationalize the tentative concept descriptions, but the obtained results are tested on the whole learning set rather than on a single example. Moreover, deduction is interleaved with the application of data-driven inductive steps. In this way, a search in a constrained space of possible descriptions can help overcome some limitations of the domain theory (e.g., inconsistency). The method has been tested in the framework of the inductive learning system “ML-SMART,” previously developed by the authors, and a simple example is also given.read more
Citations
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Book ChapterDOI
Lifelong learning algorithms
TL;DR: Machine learning has not yet succeeded in the design of robust learning algorithms that generalize well from very small datasets, while humans often generalize correctly from only a single training example, even if the number of potentially relevant features is large.
Journal ArticleDOI
Learning hard concepts through constructive induction: framework and rationale
Larry A. Rendell,Raj Seshu +1 more
TL;DR: This work argues for a specific approach to constructive induction that reduces variation by incorporating various kinds of domain knowledge, i.e., transformations that group together non‐contiguous portions of feature space having similar class‐membership values.
Journal ArticleDOI
Generalizing Version Spaces
TL;DR: This paper describes how three examples of very different types of information—ambiguous data, inconsistent data, and background domain theories as traditionally used by explanation-based learning—can each be used by the new version-space approach.
Proceedings Article
Tractable induction and classification in first order logic via stochastic matching
Michèle Sebag,Céline Rouveirol +1 more
TL;DR: Stochastic matching is used for polynomial induction and use of FOL hypotheses with no size restrictions, to allow for resource-bounded learning, without any a priori knowledge about the problem domain.
Journal ArticleDOI
Grammatically biased learning: learning logic programs using an explicit antecedent description language
TL;DR: A learning system is described that makes a large part of the concept description language an explicit input, and some of the possible applications of providing this additional input are discussed.
References
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Journal ArticleDOI
A theory and methodology of inductive learning
TL;DR: The authors view inductive learning as a heuristic search through a space of symbolic descriptions, generated by an application of various inference rules to the initial observational statements, including generalization rules, which perform generalizing transformations on descriptions, and conventional truth-preserving deductive rules.
Book ChapterDOI
A theory and methodology of inductive learning
TL;DR: The presented theory views inductive learning as a heuristic search through a space of symbolic descriptions, generated by an application of various inference rules to the initial observational statements.
Journal ArticleDOI
Explanation-based generalization: a unifying view
TL;DR: This paper proposed a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization, which is illustrated in the context of several example problems, and used to contrast several existing systems for explanation based generalization.
Journal ArticleDOI
Explanation-Based Learning: An Alternative View
Gerald DeJong,Raymond J. Mooney +1 more
TL;DR: Six specific problems with the previously proposed framework for the explanation-based approach to machine learning are outlined and an alternative generalization method to perform explanation- based learning of new concepts is presented.
Journal ArticleDOI
Incremental Learning from Noisy Data
TL;DR: This paper first reviews a framework for discussing machine learning systems and then describes STAGGER in that framework, which is based on a distributed concept description which is composed of a set of weighted, symbolic characterizations.