Sentence examples for models learning is from inspiring English sources

The fast adaptation is realized by the data re-initialization and switching between different models; learning is realized by recording and retrieving the trained up models.

We show that, as in the standard model, learning is complete if and only if signals are unbounded.

Our method of selecting training viewpoints is directly related to the kernel parameters set in (4) to ensure that model learning is effective and efficient.

In this model learning is ongoing and not separable from context.

The idea of disrupted internal model learning is particularly intriguing in relation to the cerebellum-autism connection.

The Maximum Entropy method (Nigam et al., 1999) for model learning is based on the principle that in the absence of prior knowledge, the least informative distribution, i.e. the distribution with the maximum entropy, is preferred.

The models learned are used to drive a synthetic agent that can interact with the world in a semi-natural way.

Then the dynamic time order network and its model learning are presented.

Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing.

To avoid data-overfitting in Bayesian network learning [ 5], each model learned is only allowed to have a small number of parent nodes (i.e. regulatory rules).

Notably, the relative balance between model-based and model-free learning is, in this framework, captured by a single weight parameter (w), which is larger when model-based learning is relatively more dominant.