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In many classifications tasks the test data should have the same distribution of outcome variables.
For the purpose of data collection from verbal autopsies, categories such as "fetal," "cord," and "placenta," typically found in many classifications, could easily be combined, e.g., into "other intrauterine," as exemplified in Figure 5.
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In many classification cases, the labeled samples are difficult to acquire.
Since labeling problem exists in many classification fields, and SSL_3C can effectively decrease manual labeling workload, then the proposed SSL_3C should find widespread applications in many other fields.
Although the SVM outperforms the perceptron in many classification tasks it does not lend itself to a hardware implementation as readily because it cannot be trained incrementally.
Further, comparing to representing the video activity as a time series of covariance matrices, the BoW model is more efficient and has been shown to be effective in many classification tasks.
We show by means of both synthetic and real world data sets that the proposed SVP is able to provide very accurate results in many classification problems, providing maximal margin solutions when classes are separable, and also producing very compact architectures comparable to classical multilayer perceptrons.
Support vector machine (SVM) was introduced by Vapnik [28], and has a comprehensive applications in many classification and regression problems.
Support vector machine is a supervised, multivariate classification method [37] with optimal empirical performance in many classification settings [38] that has previously been utilized in neuroimaging research [2] [3], [7], [9].
SVMs have been effective in many classification problems.
Support Vector Machines (SVMs) are widely used in many classification and regression problems.
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