Results 301 to 310 of about 93,680 (316)

Cautious induction in inductive logic programming

1997
Many top-down Inductive Logic Programming systems use a greedy, covering approach to construct hypotheses. This paper presents an alternative, cautious approach, known as cautious induction. We conjecture that cautious induction can allow better hypotheses to be found, with respect to some hypothesis quality criteria.
Simon Anthony, Alan M. Frisch
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Grammar Induction as Substructural Inductive Logic Programming

2000
In this chapter we describe an approach to grammar induction based on categorial grammars: the EMILE algorithm. Categorial grammars are equivalent to context-free grammars. They were introduced by Ajduciewicz and formalised by Lambek. Technically they can be seen as a variant of the propositional calculus without structural rules.
Erik de Haas, Pieter Adriaans
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The case for inductive programming

Computer, 1997
The science of creating software is based on deductive methods, but induction-deduction's ignored sibling-could have a profound effect on the future development of computer science theory and practice. Inductive reasoning can solve problems outside the realm of machine learning.
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The Effectiveness of Induction Programs

Kappa Delta Pi Record, 1989
(1989). The Effectiveness of Induction Programs. Kappa Delta Pi Record: Vol. 25, No. 4, pp. 117-119.
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Inductive logic programming

New Generation Computing, 1991
A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears.
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Phonotactics in Inductive Logic Programming

2004
We examine the results of applying inductive logic programming (ILP) to a relatively simple linguistic task, that of recognizing monosyllables in one language. ILP is suited to linguistic problems given linguists' preference for formulating their theories in discrete rules, and because of ILP's ability to incorporate various background theories. But it
John Nerbonne, Stasinos Konstantopoulos
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Bayesian Inductive Logic Programming

Proceedings of the seventh annual conference on Computational learning theory - COLT '94, 1994
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learning and Computational Learning Theory, ILP is based on lock-step development of Theory, Implementations and Applications.
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Programming with Inductive and Co-Inductive Types

1992
We look at programming with inductive and co-inductive datatypes, which are inspired theoretically by initial algebras and final co-algebras, respectively. A predicative calculus which incorporates these datatypes as primitive constructs is presented. This calculus allows reduction sequences which are significantly more efficient for two dual classes ...
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Support Vector Inductive Logic Programming [PDF]

, 2005
In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP). We propose a general method for constructing kernels for Support Vector Inductive Logic Programming (SVILP).
Huma Lodhi, Stephen Muggleton, Ata Amini, Michael J.E. Sternberg   +3 more
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Inductive methods for proving properties of programs

ACM SIGPLAN Notices, 1972
We have two main purposes in this paper. First, we clarify and extend known results about computation of recursive programs, emphasizing the difference between the theoretical and practical approaches. Secondly, we present and examine various known methods for proving properties of recursive programs. We discuss in detail two powerful inductive methods,
Jean Vuillemin, Zohar Manna, Stephen Ness   +2 more
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