Sentence examples for algorithm for learning the from inspiring English sources

Back-propagation, since 1980s, has been a well-known algorithm for learning the weights of neural networks [3, 4], and widely used in deep learning networks.

Trying to apply the QBC algorithm for learning the class of linear separators, one faces the problem of implementing the mechanism of sampling hypotheses (the Gibbs oracle).

The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method.

It may well be that there is a simple algorithm for learning the meaning of 'true' and that, consequently, there is no special difficulty in learning to apply the concept.

We summarize this discussion by sketching an algorithm for learning the proposed semi-supervised classifier: 1. Construct the set of equations (4), possibly by replacing the graph Laplacian L with Lnk.   2.

Monotonicity: Q ⊆ T ⇒ μ Q ≤ μ T. In this work, a supervised gradient-based training algorithm for learning the fuzzy measures from the training data with cross-validation is used [18, 19].

We then describe a variety of algorithms for learning the structure of a network from observational data.

Two different evolutive algorithms are use for learning the models: the well-known NSGA-II genetic algorithm and a multi-objective simulated annealing algorithm hybridized with genetic operators.

Specifically, we proposed a novel ℓ2,1 norm balanced multiple kernel feature selection (ℓ2,1 MKFS), and designed a proximal based optimization algorithm for efficiently learning the model.

In order to obtain a gene-interaction network to use as an input to GFlasso, we used the algorithm for learning a topological overlap matrix as described in Zhang & Horvath [ 14] that was applied to the same dataset in Zhu et al. [ 9].

This results in a new O(n) algorithm for learning disjunctions which is related to the Bylander's BEG algorithm originally introduced for linear regression.