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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.
Our experiments were conducted using a linear kernel, known to perform well in document classification.
The predictions are performed using a linear kernel SVM classifier with the default complexity parameter C =5 (Fan et al. 2008 ).
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 classification of the hippocampus samples was performed using a linear kernel with a gamma = 0.01 and cost = 1 (library e1071, version 1.6-1).
For the marker-based SPMMs, a genetic kernel matrix calculated using a linear kernel matrix incorporates only additive effects of markers.
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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.
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.
Note that Equation (17) uses a linear kernel, where.
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.
More suggestions(15)
using a linear correlation
using a linear production
using a linear relationship
using a novel kernel
using a gamma kernel
using a linear fit
using a linear stapler
using a software kernel
using a linear probability
using a linear function
using a diffusion kernel
using a coalescence kernel
using a uniform kernel
using a linear high-frequency
using a linear calibration
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