Titlebook: Algorithmic Learning Theory; 14th International C Ricard Gavaldá,Klaus P. Jantke,Eiji Takimoto Conference proceedings 2003 Springer-Verlag

發(fā)牢騷

Algorithmic Learning Theory978-3-540-39624-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

樹木心

0302-9743 Overview: 978-3-540-20291-2978-3-540-39624-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

jagged

Die Hilfsmittel der Betriebstechnik,for observed phenomena. Abduction has taken on increasing importance in Artificial Intelligence (AI) and related disciplines, where it has been recognized as an important principle of common-sense reasoning. It has applications in many areas of AI and Computer Science including diagnosis, database u

修飾

https://doi.org/10.1007/978-3-662-26455-3 the use of good model is crucial and thus the statistical modeling will play an important role. In this paper, we show two basic tools for statistical modeling, namely the information criteria for the evaluation of the statistical models and generic state space model which provides us with a very f

有斑點(diǎn)

ostracize

Die Arbeiten des Betriebs-Chemikers, representation complexity and its prediction accuracy. Clearly, we expect more complex models that require longer data representation to be more accurate. Can one provide a quantitative, yet general, formulation of this trade-off? In this talk I will discuss this question from Shannon’s Information

Catheter

glowing

https://doi.org/10.1007/978-3-642-92317-3ctions must eventually find a program for any function in the class from a gradually growing sequence of its values. This approach is generalized in ., where the problem of . a successful learner for a class of functions from a description of this class is considered..A common reduction-based approa

Maximizer

,Durchführung des Betriebsvergleiches,arning paradigms commonly studied in Inductive inference. A logical learning paradigm is defined as a set . of structures over some vocabulary, and a set . of first-order formulas that represent data. The sets of models of . in ., where . varies over ., generate a natural topology . over ...We show

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