Sentence examples for learning input from inspiring English sources

Guetig, R. et al. Learning input correlations through nonlinear temporally asymmetric hebbian plasticity.

It includes fuzzy partition generation, rule learning, input space reduction and rule base simplification.

After learning, input from the basolateral complex to the central nucleus of the amygdala leads to the orchestration of a range of physiological and behavioral responses that are correlated with emotional states.

Voronoi diagram is constructed over points in feature space which represents learning input vectors in order to realize the desired classification.

In order to reduce the size of neural network and to make the learning process efficiently, clustering procedure which enables to manage a number of learning input vectors in a lump is implemented Two examples demonstrate that the present method gives stable result and a calculation time is smaller than that of back propagation method.

And the biases of Externalist approaches to language acquisition are to be found in the distributional and stochastic structure of the learning input and the multitude of mechanisms that process that input and their interactions.

Weekend and summer heat has little impact and the effect is not explained by pollution or local economic shocks, suggesting heat directly reduces the productivity of learning inputs.

The startup's free service essentially scores and validates a host of different learning inputs, whether they be from formal institutions, like the University of California, or informal platforms like Khan, Lynda.com, iTunesU, Coursera and so on.

The HGA approach is composed by five hierarchical levels where the following parameters of the T S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules.

The convolution layer learns input features through the convolution of the kernel and the input vectors, as shown in Fig. 4.

To obtain asymmetric convergence, we had to consider morphs between a "learned" input pattern, used in training the network, and a novel pattern (simulation 2, Fig. 7 d ).