Les professionnels de l'information traversent actuellement une période de redéfinition de leur profession provoquée par la transformation de l'information et des processus informationnels vers un mode de plus en plus électronique. Les systèmes d'information Web (SIW) — c'est-à-dire des systèmes…

Much Learning is done by way of studying precedents and exercises. A teacher supplies a story, gives a problem, and expects a student both to solve a problem and to discover a principle. The student must find the correspondence between…

Well-known methods for solving the shape-from-shading problem require knowledge of the reflectance map. Here we show how the shape-from-shading problem can be solved when the reflectance map is not available, but is known to have a given form with some…

There are many situations in which a very detailed low-level description encodes, through a hierarchical organization, a recognizable higher-order pattern. The macro-molecular structural conformations of proteins exhibit higher order regularities whose recognition is complicated by many factors. ARIADNE searches for…

Well-known methods for solving the shape-from-shading problem require knowledge of the reflectance map. Here we show how the shape-from-shading problem can be solved when the reflectance map is not available, but is known to have a given form with some…

Morphological analysis requires knowledge of the stems, affixes, combnatory patterns, and spelling-change processes of a language. The computational difficulty of the task can be clarified by investigating the computational characteristics of specific models of morphologial processing. The use of finite-state…

This paper presents a novel algorithm for learning in a class of stochastic Markov decision processes (MDPs) with continuous state and action spaces that trades speed for accuracy. A transform of the stochastic MDP into a deterministic one is presented…

We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this paper we show that regularization networks encompass a much…

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