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Supervised Learning (SL) is a machine learning research area which aims at developing techniques able to take advantage from labeled training samples to make decisions over unseen examples. Recently, a lot of tools have been presented in order to perform machine learning in a more straightforward and transparent manner. However, one problem that is increasingly present in most of the SL problems being...
The paper proposes an adaptive classification mechanism designed for structured light system to improve quality of reconstructed models. We observed that the conventional albedo-based thresholding fails when the lighting condition is not carefully considered. To address this problem, an adaptive model is proposed. The core idea is to adjust decision boundary during extraction of sequence of binary-coded...
In this paper we present a method for learning class-specific features for recognition. Recently a greedy layer-wise procedure was proposed to initialize weights of deep belief networks, by viewing each layer as a separate restricted Boltzmann machine (RBM). We develop the convolutional RBM (C-RBM), a variant of the RBM model in which weights are shared to respect the spatial structure of images....
This paper presents a novel approach to microcalcification clusters (MCs) detection in mammograms based on the tensor subspace learning and twin support vector machines (TWSVMs). The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is enhanced by using a simple artifact removal filter and a well designed high-pass filter. Then the tensor subspace learning algorithms,...
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