Abstract

Abstract
This paper describes the work on methods for combining rules obtained by machine learning systems. Three methods for obtaining the classification of examples with those rules are compared. The advantages and disadvantages of each method are discussed and the results obtained on three real world domains are commented. The methods compared are: selection of the best rule; PROSPECTOR-like probabilistic approximation for rule combination; and MYCIN-like approximation. Results show significant differences between methods indicating that the problem-solving strategy is important for accuracy of learning systems. © Springer-Verlag Berlin Heidelberg 1993.

Abstract
This paper introduces a new concept learning system. Its main features are presented and discussed. The controlled use of redundancy is one of the main characteristics of the program. Redundancy, in this system, is used to deal with several types of uncertainty existing in real domains. The problem of the use of redundancy is addressed, namely its influence on accuracy and comprehensibility. Extensive experiments were carried out on three real world domains. These experiments showed clearly the advantages of the use of redundancy. © Springer-Verlag Berlin Heidelberg 1993.

Abstract

Abstract
Symbolic objects (Diday (1987, 1992), Brito, Diday (1990), Brito (1991)) allow to model data on the form of descriptions by intension, thus generalizing the usual tabular model of data analysis. This modelisation allows to take into account variability within a set. The formalism of symbolic objects has some notions in common with VL1, proposed by Michalski (1980); however VL1 is mainly based on prepositional and predicate calculus, while the formalism of symbolic objects allows for an explicit interpretation within its framework, by considering the duality intension-extension. That is, given a set of observations, we consider the couple (symbolic object — extension in the given set). This results from the wish to keep a statistics point of view. The need to represent non-deterministic knowledge, that is, data for which the values for the different variables are assigned a weight, led to considering an extension of assertion objects to probabilist objects (Diday 1992). In this case, data are represented by probability distributions on the variables observation sets. The notions previously defined for assertion objects are the generalized to this new kind of symbolic objects. Other extensions can be found in Diday (1992). © Springer-Verlag Berlin Heidelberg 1993.

Abstract
In this paper we introduce the generalized feedforward filter, a new class of adaptive filters that combines attractive properties of finite impulse response (FIR) filters with some of the power of infinite impulse response (IIR) filters. A particular case, the gamma filter, generalizes Widrow's adaptive transversal filter (adaline) to an infinite impulse response filter. Yet, the stability condition for the gamma filter is trivial, and least mean square (LMS) adaptation is of the same computational complexity as the conventional transversal filter structure. Preliminary results indicate that the gamma filter is more efficient than the adaptive transversal filter. We extend the Wiener-Hopf equation to the gamma filter and develop some analysis tools.