Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Bounding Rational Analysis: Constraints on Asymptotic Performance

Abstract: Critical of mechanistic accounts of cognition, Anderson (1990) showed that a demonstration that cognition is optimally adapted to its purpose and environment can offer an explanation for its structure. Simon (1992), in contrast, emphasised that the study of an adaptive system is not a " logical study of optimization, " but an empirical study of the conditions that limit the approach to the optimum. In response, we sketch the requirements for an approach to explaining behaviour that emphasises explanations in terms of the optimal behaviour given not only descriptions of the objective and environment but also descriptions of the human cognitive architecture and knowledge. A central assumption of the proposal is that a theory explains behaviour if the optimal behaviour predicted by the theory shows substantial correspondence to asymptotic human performance.

Fast and Tight Fitting Bounding Spheres

Abstract: ReferencesCitations inCitations in Google ScholarBounding spheres are utilized frequently in many computer graphics and visualization applications; and it is not unusual that the computatio ...

A novel approach to state bounding for discrete-time Markovian jump systems with interval time-varying delay

Abstract: This paper concerns with the problem of state bounding for a class of discrete-time Markovian jump systems with interval time-varying delay. By constructing a new Lyapunov–Krasovskii functional combining with the delay-decomposition technique and the reciprocally convex approach, new delay-range-dependent conditions are derived ensuring that all state trajectories of the system are mean square bounded. These conditions are derived in terms of matrix inequalities which can be computationallysolved to find a smallest possible bound of mean square. A numerical example is provided to verify the effectiveness of the obtained result.

Support vector machine classification via parameterless robust linear programming

Abstract: We show that the problem of minimizing the sum of arbitrary-norm real distances to misclassified points, from a pair of parallel bounding planes of a classification problem, divided by the margin (distance) between the two bounding planes, leads to a simple parameterless linear program. This constitutes a linear support vector machine (SVM) that simultaneously minimizes empirical error of misclassified points while maximizing the margin between the bounding planes. Nonlinear kernel SVMs can be similarly represented by a parameterless linear program in a typically higher dimensional feature space. Data Mining Institute Technical Report 03-01, March 2003. Revised March 2004.