Abstract

Abstract

Abstract
Due to the wide variety of algorithms for supervised classification originating from several research areas, selecting one of them to apply on a given problem is not a trivial task. Recently several methods have been developed to create rankings of classification algorithms based on their previous performance. Therefore, it is necessary to develop techniques to evaluate and compare those methods. We present three measures to evaluate rankings of classification algorithms, give examples of their use and discuss their characteristics.

Abstract
We propose a Multi-Agent framework to analyze the dynamics of organizational survival in cooperation networks. Firms can decide to cooperate horizontally (in the same market) or vertically with other firms that belong to the supply chain. Cooperation decisions are based on economic variables. We have defined a variant of the density dependence model to set up the dynamics of the survival in the simulation. To validate our model, we have used empirical outputs obtained in previous studies from the automobile manufacturing sector. We have observed that firms and networks proliferate in the regions with lower marginal costs, but new networks keep appearing and disappearing in regions with higher marginal costs.

Abstract

Abstract
In this paper we are concerned with the problem of inducing recursive Horn clauses from small sets of training examples. The method of iterative bootstrap induction is presented. In the first step, the system generates simple clauses, which can be regarded as properties of the required definition. Properties represent generalizations of the positive examples, simulating the effect of having larger number of examples. Properties are used subsequently to induce the required recursive definitions. This paper describes the method together with a series of experiments. The results support the thesis that iterative bootstrap induction is indeed an effective technique that could be of general use in ILP.