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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Machine learning 19 (1995), S. 95-131 
    ISSN: 0885-6125
    Keywords: theory revision ; knowledge refinement ; inductive logic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performingtheory refinement. This paper presents a system,Forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole.Forte uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including propositional theory refinement, first-order induction, and inverse resolution.Forte is demonstrated in several domains, including logic programming and qualitative modelling.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Machine learning 19 (1995), S. 95-131 
    ISSN: 0885-6125
    Keywords: theory revision ; knowledge refinement ; inductive logic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 0749-1581
    Keywords: 13C NMR ; 2-Iodosobenzoic acids ; 2-Iodoxybenzoic acids ; Sodium 2-iodosobenzoates ; Sodium 2-iodoxybenzoates ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The 13C NMR spectra for selected 2-iodosobenzoic acids, their sodium salts and the sodium salts of the corresponding iodoxybenzoic acids were measured and carbon assignments made using 2D and NOE experiments and relaxation times. Iodoso and iodoxy groups deshielded the ipso-carbon in these compounds by approximately 25 and 55 ppm, respectively, relative to the corresponding iodo compounds. The 13C NMR spectra of the 2-iodosobenzoate anions were almost identical with those of the corresponding free acids, both possessing cyclic structures. The iodoxybenzoic acids are either insoluble in, or oxidize, suitable NMR solvents (i.e. DMSO-d6, DMF-d7). The cyclic structure postulated for iodoxybenzoate anions is supported by their 13C chemical shifts.
    Additional Material: 1 Ill.
    Type of Medium: Electronic Resource
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