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Also we use a linear kernel (rather than a pyramid one) during SVM classification, whereas a similarity measure based on Bhattacharyya coefficient [21] (instead of distance) when KNN is employed for classification purpose.
Different sources of data are combined by adding linear kernels at the input-space level, and for the output space we use a linear kernel between label vectors.
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We used a linear kernel function as it is considered more efficient for real-time applications [51].
We have used a linear kernel with default parameter settings since this was shown to give better results than a radial basis function in our initial experiments.
The SVM developed a model based on the labels relative to the features and created a discriminant method using a linear kernel with default parameters to predict plausible target genes from paralogs.
Note that Equation (17) uses a linear kernel, where.
Our experiments were conducted using a linear kernel, known to perform well in document classification.
As a baseline, we trained (and evaluated in an analogous setting) the six models independently on each domain, both using a linear kernel and a Gaussian compounded kernel.
For the marker-based SPMMs, a genetic kernel matrix calculated using a linear kernel matrix incorporates only additive effects of markers.
All classifiers were built using SVM light http://svmlight.joachims.org/ using a linear kernel option and complete leave-one-out estimations were calculated for each experiment.
The SVM model used a linear kernel function and default parameters (a fixed regularization parameter C = 1) to compute a hyperplane that best separated the trial responses.
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