Abstract

Abstract

Abstract
This paper presents a new method that deals with a supervised learning task usually known as multivariate regression. The main distinguishing feature of this new technique is the use of a clustering method to obtain sub-sets of the training data before the learning phase. After this "resampling" process a different regression model is fitted to each found cluster. We call the resulting method clustered partial linear regression. Predictions using this technique are preceded by a cluster membership query for each test case. The cluster membership probability of a test case is used as a weight in an averaging process that calculates the final prediction. This averaging process involves the predictions of the regression models associated to the clusters for which the test case may belong. We have tested this general multi-strategy approach using several regression techniques and we have observed significant accuracy gains in several data sets. We have also compared our method to bagging that also uses an averaging process to obtain predictions. This experiment showed that the two methods are significantly different. Finally, we present a comparison of our method with several state-of-the-art regression methods.

Abstract
This text describes a real-world DMS environment in which intelligent tools and techniques such as neural networks, fuzzy sets and meta-heuristics (like evolutionary computing and simulated annealing) have given a strong positive contribution.

Abstract
In this paper, we present some original theoretical aspects of a fast nonlinear association measure based on the work of Cramer. The features of this new measure-the V measure-when applied to biosignals are also shown using simulated time series. A comparative study with other well-known association measures available in the literature of biosignals is presented, V was found to be twice as fast and more robust to nonlinearities than the classical cross-correlation ratio (r(2)) and more than 100 times faster than the nonlinear regression coefficient (h(2)), presenting similar behavior in the presence of nonlinear simulated situations. This new measure is very fast and versatile, It is appropriate to deal with nonlinear relations presenting usually a sharp peak in the association function enabling a high degree of selectivity for maxima detection. It seems to constitute an improvement over linear methods of association which is faster and more robust to the existing nonlinearities. It can be used as an alternative to more complex nonlinear association measures when computational speed is an important feature.

Abstract
In this paper, we describe an approach to allocation of function that makes use of scenarios as its basic unit of analysis. Our use of scenarios is driven by a desire to ensure that allocation decisions are sensitive to the context in which the system will be used and by insights from economic utility theory. We use the scenarios to focus the attention of decision makers on the relative costs and benefits of developing automated support for the activities of the scenario, the relative impact of functions on the performance of the operator's primary role and on the relative demands placed on an operator within the scenario. By focussing on relative demands and relative costs, our method seeks to allocate the operator's limited resources to the most important and most productive tasks within the work system, and to direct the effort of the design organization to the development of automated support for those functions that deliver the greatest benefit for the effective operation of the integrated human-machine system.