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In this paper, we propose a hybrid diagnostic system based on discretization (quantization) method and classification algorithms including C4.5 decision tree classifier, artificial neural network (ANN), and least square support vector machine (LSSVM) to diagnose the optic nerve disease from Visual Evoked Potential (VEP) signals with discrete values.
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The iterative local method is based on discretization of the design space with truss elements.
Our approach is based on discretization and anti-discretization methods developed in [4, 7, 8] and [6].
Approximation is based on discretization of the real traffic pattern f R (t) using three distinct approximation approaches.
The FEM framework is based on discretization of the continuous domain and function spaces (Hadjicharalambous et al. 2014).
In this article, we aim to bridge the gap between continuous and Hamming domains, and provide a revelation upon how discretization based on equal-width quantization and linearly separable subcode encoding could affect the classification performance in the Hamming domain.
Our method is based on vector quantization, which is one of the popular image compression techniques.
In this paper, we propose a new feedback design based on vector quantization.
This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ).
New evidence based on the quantization effect indicates a high degree of adjacent re-entry in regime I for the lower molecular weight fractions.
We propose algorithms based on uniform quantization and sequential search to solve the corresponding non-convex optimization problems.
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