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Abstract Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued

Iterative decoding of two-dimensional systematic convolutional codes has been termed “turbo” (de)coding. Using log-likelihood algebra, we show that any decoder can he used which accepts soft inputs-including a priori values-and delivers soft outputs that can he split into three terms: the soft

are implemented in simulation, on robots in the laboratory and aboard DARPA's HMMWV-based Unmanned Ground Vehicles. The technique has been integrated with the Autonomous Robot Architecture (AuRA) and the UGV Demo II architecture. The results demonstrate the value of various types of formations in autonomous

to be related to appropriate sets of salient behavioral, normative, and control beliefs about the behavior, but the exact nature of these relations is still uncertain. Expectancy — value formulations are found to be only partly successful in dealing with these relations. Optimal rescaling of expectancy

framework in which the moving-average root is local to −1 to document how the MIC performs better in selecting appropriate values of k. In monte-carlo experiments, the MIC is found to yield huge size improvements to the DF GLS and the feasible point optimal PT test developed in Elliott, Rothenberg and Stock

clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our

and verification of real-time systems, based on constraint--solving and on-the-fly techniques, developed jointly by Uppsala University and Aalborg University. It is appropriate for systems that can be modeled as a collection of nondeterministic processes with finite control structure and real-valued clocks

We examine how reduction of search frictions in secondary markets affects the ability of incumbents in primary markets to appropriate value. We argue that the effect depends on the relative strength of the added value and cannibalization effects, which in turn is contingent on the separability

nothing directly to do with coding or decoding will show that in some sense belief propagation "converges with high probability to a near-optimum value" of the desired belief on a class of loopy DAGs Progress in the analysis of loopy belief propagation has been made for the case of networks