Sentence examples for vector learning from inspiring English sources

The Laplacian regularized least squares (LapRLS) is a classical vector learning algorithm following this framework.

Secondly, the support vector learning algorithm finds the number and location of centers and the weights of the network.

First, the nonlinear process with measurement noise is regressed by the relevance vector learning mechanism based on a kernel-based Bayesian framework.

The model is based on a radial basis function network architecture and uses subtractive clustering and support vector learning to find the parameters and size of the network.

In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented.

This paper proposes a self-splitting fuzzy classifier with support vector learning in expanded high-order consequent space (SFC-SVHC) for classification accuracy improvement.

Really, the main idea of the proposed novel mutation is based on that each vector learns from the position of the top best and the bottom worst individuals among the entire population of a particular generation.

Open image in new window Fig. 4 Example basis vectors learned by the original NMF.

The number of support vectors learned by the SVM classifier is equal to approximately half of the training patterns.

This is an asymmetric transformation to map the weight vectors learned from the binary classifiers of the source domain (as multi-class problems are commonly decomposed into multiple binary classifiers) to those of the target.

Support Vector Data Description (SVDD) is a support vector based learning algorithm for anomaly detection.