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One can represent stereo images in a better way by using a feature space where they are robust, distinct, and transformation invariant [11, 12].
E-SSpred reached a Matthews correlation coefficient of 0.44, but they were the first to break the 80% accuracy (Qtotal) barrier and achieved a Qtotal of 80.9%, compared to 77.3% by Zhang et al. Zheng and Kurgan [21] applied support vector machines using a feature space consisting of position specific scoring matrices and secondary structure predictions from four different methods.
This is the relevant situation in a clinical setting, because data from a patient is classified using a feature space populated by data from other patients, and whose C-scans where acquired at different time points.
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There are several studies that use a feature space centroid to generate new data.
Kwok and his colleagues used a feature space centroid to generate a new data point for hand-written digit recognition [29] and speech processing [30].
In addition to its improved performance, our method uses a feature space with a much lower dimensionality.
The third step was to design or to generate a new point in the kernel feature space using a kernel feature space algorithm.
The third step is to design or to generate a new point in the kernel feature space using a kernel feature space algorithm (e.g. the center of highly active compounds).
Therefore, whatever model is used in such a feature space will be trained on scattered samples which are not filling the feature space at all.
The resulting algorithm is named VQ_fk_nps (Vector Quantization for data clustering in a Feature space using the Gaussian Kernel function and the Nearest Prototype Strategy).
SVM takes as input a vector and maps it into a feature space using a kernel function [ 18, 19].
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