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The prediction performance and robustness of artificial neural network depends on selection of training data owing to basic nature of neural network to learn from training through back propagation.
ANN can predict the correct class of the received signal even if the input signals have not been seen before, which allow the model to learn from training dataset and generalize the model to any received signal.
Support vector machines (SVMs) are computational algorithms that can learn from training examples for binary classification problems.
IT systems using machine learning methods are capable to learn from training datasets and predict possible outcomes based on new observations.
From a learning systems point of view, similarly to artificial neural networks [ 22, 23] and inductive inference systems [ 24] that learn from training examples, a CBR system acquires new knowledge, stores it in a case base and makes use of it in new problem solving situations.
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In which, the problem is considered as a classification one, where a classifier is learned from training data; then the learned classifier is used to predict whether or not a test/candidate gene is a disease gene.
One of the features of such models is their capability of automatically learning from training examples.
The soft membership is determined with two threshold values which can be learned from training data.
The potentials are represented using nonparametric kernel densities and are learnt from training data.
The parameters are learned from training data using Baum-Welch method.
Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.
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