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Titlebook: Algorithmic Learning Theory; 12th International C Naoki Abe,Roni Khardon,Thomas Zeugmann Conference proceedings 2001 Springer-Verlag Berlin

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Loss Functions, Complexities, and the Legendre Transformationonding predictive complexity w.r.t. the Bernoulli distribution are related through the Legendre transformation. It is shown that if two loss functions specify the same complexity then they are equivalent in a strong sense.
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Editors’ Introduction statistics. The main thrust is an attempt to model learning phenomena in precise ways and study the mathematical properties of these scenarios. In this way one hopes to get a better understanding of the learning scenarios and what is possible or as we call it learnable in each. Of course this goes
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Robot Baby 2001. We discuss several kinds of meaning that representations might have, and focus on a functional notion of meaning as appropriate for programs to learn. Specifically, a representation is meaningful if it incorporates an indicator of external conditions and if the indicator relation informs action. W
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Discovering Mechanisms: A Computational Philosophy of Science Perspectiveuation and revision. Because mechanisms are often what is discovered in biology, a newc haracterization of mechanism aids in their discovery. A computational system for discovering mechanisms is sketched, consisting of a simulator, a library of mechanism schemas and components, and a discoverer for
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Inventing Discovery Tools: Combining Information Visualization with Data Miningearchers believe in the importance of giving users an overview and insight into the data distributions, while data mining researchers believe that statistical algorithms and machine learning can be relied on to find the interesting patterns. This paper discusses two issues that influence design of d
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