The majority rule scheme has been applied in the setting of robot swarms as a mechanism to reach consensus among a population of robots regarding the optimality of one out of two options. In the context of distributed decision making for agents, we consider two schemes of combining the majority rule scheme with dynamic adaptation for the well-known double bridge problem to cater for a situation...

The idea of combining classifiers in order to compensate their individual weakness and to preserve their individual strength has been widely used in recent pattern recognition applications. In this paper, the cooperation of two feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers will be examined. We investigate the advantages and weaknesses of vario...

In order to solve the overfitting of sample weights and the low detection rate in training process of the traditional AdaBoost algorithm, an improved AdaBoost algorithm based on Haar-like features and LBP features is proposed. This method improves weight updating rule and weights normalization rule of the traditional AdaBoost algorithm. Then combining this method with the AdaBoost algorithm bas...

We analyze a model of resource allocation in which agents’ abilities (to transform the resource into an interpersonally comparable outcome) and initial endowments may differ. We impose ethical and operational axioms in this model and characterize some allocation rules as a result of combining these axioms. Two focal (and polar) egalitarian rules are singled out. On the one hand, the rule that a...

Model combining (mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been demonstrated in simulations and data examples, it is still unclear to a large extent when model combining should be preferred. In this work, firstly, an instability measure to capture the uncertainty of model se...

Assessing rule interestingness is the cornerstone of successful applications of association rule discovery. In this article, we present a new measure of interestingness named IPEE. It has the unique feature of combining the two following characteristics: first, it is based on a probabilistic model, and secondly, it measures the deviation from what we call equilibrium (maximum uncertainty of the...

A recent development in the hybrid HMM/ANN speech recognition paradigm is the use of several subword classifiers, each of which provides different information about the speech signal. Although the combining methods have obtained promising results, the strategies so far proposed have been relatively simple. In most cases frame-level subword unit probabilities are combined using an unweighted pro...

Association rule mining often requires the repeated execution of some extraction algorithm for different values of the support and confidence thresholds, as well as for different source datasets. This is an expensive process, even if we use the best existing algorithms. Hence the need for incremental mining, whereby mining results already obtained can be used to accelerate subsequent steps in t...

Software agents are often seen as “intelligent, autonomous software components.” Interestingly, the question of efficient implementation of “intelligence” remains open. In this paper we discuss, in some details, the process of implementing software agents with “brains.” In the context of an agent system supporting decisions of glider pilots, we consider native implementation of “intelligent” be...

We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule devel...