Sentence examples for samples trained with from inspiring English sources

Ng, Ong and Noor [43] highlighted issues with classical edge detectors (e.g. rounded edges) and proposed a neural edge detector, with a hybrid approach (partially supervised and partially unsupervised) using a MLP network with input data based on 3×3 image samples, trained with only five images.

The rear-SVM classifier is trained with 2000 samples and tested with 1000 samples (1/1 positive/negative ratio) whereas the forward-SVM classifier is trained with 3000 samples and tested with 2000 samples (1/1 positive/negative ratio).

For instance, Xing et al. (2010) identified the parameters on three independent sampling trains with different initial parameters and then used two types of validation.

For non-edge training samples, xedge=0 and x¬edge=1, whereas for edge samples, x¬edge=0 and xedge was trained with an estimate of the edge's local contrast c (with 1 being the maximum contrast).

It allows a classifier trained with sample views of a particular object to be detected in a whole image.

Therefore, it is able to use the two variables z 1 and z 2 to describe the contour of the objective function Y on the plane in case of the neural network trained with sample data.

Network is trained with all samples of one class as positive label and rest samples with negative label.

Each SVM modules is trained with all samples of one class as positive label and rest samples with negative label in 1-v-r SVMs (one-versus-rest).

The GRNN model was trained with 91 samples and was successfully validated with a blind testing data set of 22 samples.

A classifier is trained with case samples drawn from the patient population.

The models for sound events are trained with these samples using the Baum Welch algorithm [34].