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An SVD classifier is learnt from labeled training sequences in order to classify different actions.
In order to classify organisms according to the environmental niche, we used the SVM classifier implemented in LibSVM library (Chang and Lin 2011).
Some are so radically different that paleontologists have created new phyla in order to classify them.
Their discoveries then required detailed description and analysis, followed by comparisons with other known dinosaurs in order to classify the new finds and develop hypotheses about evolutionary relationships.
Moreover, the following definitions are significant in order to classify the four-dimensional matrices.
In order to classify and quantify these variations, linear transformation method of PCA was first applied.
In order to classify speakers the algorithm uses a simple Viterbi decoder.
In order to classify, we need to obtain fuzzy equivalence matrix by modifying fuzzy similar matrix.
The goal is to generalize from the training data in order to classify unlabeled GPR signatures.
Following this, we calculate the chemical descriptors in order to classify the collected compounds.
In order to classify participants, definitions of each category were created.
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