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Forming an efficient feature space for classification problems is a grand challenge in pattern recognition.
We exploited the multifractal analysis to represent the histologic texture, which derive more discriminative feature space for classification.
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Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification.
The basic hypothesis in using the vector space model for classification is the contiguity hypothesis.
SVM is a supervised learning method which constructs a hyperplane or set of hyperplanes in a high-dimensional space typically for classification and regression tasks.
It is a hard work to accurately choose the color space for the classification on various applications.
It studies optimal separating hyperplane in the high dimension feature space for sample classification.
Considering the high dimensionality of the feature space for text classification tasks, the most frequently used approach for feature selection is the univariate filter method [ 7].
This paper proposes a self-splitting fuzzy classifier with support vector learning in expanded high-order consequent space (SFC-SVHC) for classification accuracy improvement.
Then, in the second step, we use a kernel function to map each VSMMD to a feature space typically used for classification.
c Classification result of the ILRBF-BP algorithm Fig. 8 Using different width parameters to cover the training sample space for the twist classification problem.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com