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The kernel width of each training sample is trained by two supervised training algorithms.

Our method uses only the positive examples in the training set as a reference from which to compute the distance to the elements in the testing set, whereas TripletSVM, for each sample, is trained using both the positive and negative examples of the training set and is evaluated against the testing set.

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Generally, in LOOCV classification accuracy, N-1 samples is trained and tested on the remaining sample which has not been used during the training.

A k-nearest-neighbor (KNN) classification model for patient samples was trained on our gene expression data set of CD34+ cells with or without MYBL2 knockdown by RNAi.

With the exception of Puntland, all people involved in sampling had been trained in FMD sampling.

EyeEm says its image recognition model generalizes well with a few samples, and can be trained in near real-time using GPUs.

AAMs are trained from sample images with annotated landmark positions.

Let s1, s2, …, s N be training samples and we used the Gaussian kernel function.

The red dots and blue line are training samples and objective function, respectively.

The x i and x j are training samples and γ is a kernel parameter.

Similarly, a 0 denotes that the corresponding sample is excluded from training.

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