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This article presents a methodology to generate explicit decision functions using support vector machines (SVM).
We specifically look at methods that can generate explicit knowledge in a machine-usable form.
These methods can generate explicit computations to process incoming signals robustly in real-time even though the nonlinearities in the system may be implicitly defined.
A big reason that rational-expectations models proved so alluring to economists was their tractability: with some clever math and a computer, they could be "solved out" to generate explicit solutions for important economic variables, such as unemployment and inflation.
GW theory generates explicit predictions for conscious aspects of perception, emotion, motivation, learning, working memory, voluntary control, and self systems in the brain.
The approximations are combined with an effective sensitivity analysis of the reliability constraints in order to generate explicit expressions of the constraints in terms of the design variables.
The approximations are combined with efficient simulation methods to generate explicit approximations of reliability measures in terms of the design variables.
The approximations are combined with an efficient simulation technique to generate explicit approximations of the reliability measures with respect to the design variables.
Single-neuron recording methods, as commonly used in neuroethology studies, provide the needed spatial and temporal resolution capacities to generate explicit hypotheses addressing the 'how' of language processing.
It is shown that typical structural non-linearities such as non-linear damping and stiffness can be detected and identified directly without the need to generate explicit models.
CBM-CFS3 is a forest C budget model that utilizes growth and yield information for biomass and generates explicit simulation of dead organic matter dynamics (Kurz et al. 2009).
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CEO of Professional Science Editing for Scientists @ prosciediting.com