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Data set would be processed based on classification feature.
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More specifically, SC-trees are novel supervised hierarchical clustering trees that use node specific dictionary and classifier training to direct input images based on classification results in the feature space at each node.
Another strategy for object representation is based on classification that extracts some features characterizing the objects to generate the training set for the learning process of a classifier.
Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine.
A support vector machine (SVM) is designed for pattern classification based on the feature vector.
A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data.
In this sense, we research novel mathematical descriptors for both morphology and dynamics and will compare their performance regarding small lesion classification based on novel feature selection algorithms.
Based on the classification, operational features and detailed control schemes of AC sub-microgrid, DC sub-microgrid and interlinking converter in hybrid AC/DC microgrid are reviewed and discussed.
One can produce multiple nonexclusive gene orderings or classifications based on significant feature vectors.
In addition, Skeletonization solves the classification problem based on automatic feature extraction.
Shortest feature line segment (SFLS) is a recently proposed classification approach based on nearest feature line (NFL).
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