Titlebook: Analogical and Inductive Inference; International Worksh Klaus P. Jantke Conference proceedings 1987 Springer-Verlag Berlin Heidelberg 1987

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Conference proceedings 1987Rietz, GDR, October 16-10, 1986. Inductive inference and analogical reasoning are two basic approaches to learning algorithms. Both allow for exciting problems and promising concepts of invoking deeper mathematical results for considerable advances in intelligent software systems. Hence analogical a

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Equilibrium Speed Distributionssatisfy a recursion theoretic randomness condition. It turns out that under three natural assumptions (finite range, reliable identifiability of the function class and "proper" noise function) the identifiability is preserved up to a finite set of anomalies.

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Systems of Linear Differential Equations, some sense incomplete, data base. An algorithm is described which, using the missing information in a special way, generates the required rules. The generated rules also match an appropriate complete data base that is exponentially greater than the original one.

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Series Solutions for Differential Equations, to algorithmically determine when the correct program of the function under consideration has been found. Necessary and sufficient conditions are derived that the synthesis can be done if the admissible number of input/output examples is bounded.

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Inductive inference of functions from noised observations,satisfy a recursion theoretic randomness condition. It turns out that under three natural assumptions (finite range, reliable identifiability of the function class and "proper" noise function) the identifiability is preserved up to a finite set of anomalies.

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On the complexity of effective program synthesis, to algorithmically determine when the correct program of the function under consideration has been found. Necessary and sufficient conditions are derived that the synthesis can be done if the admissible number of input/output examples is bounded.

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