Sentence examples for in the learning algorithm from inspiring English sources

In the learning algorithm procedure, an OA-based AALA (OA-AALA) is provided to determine the optimal RBFNNs (OA-AALA-RBFNNs).

The SOM learning also provides a faithful representation of the data distribution on the prototype level, which can be controlled by a magnification parameter with slight changes in the learning algorithm [31].

This, however, has made fairly comparing extraction approaches and systems challenging because it is hard to isolate the source of improvement as it might have came from the approach itself, the features used in the learning algorithm, or the quality of the training corpus.

Also, this work brings an in-depth analysis of the solutions adopted to overcome hardware resource limitations in the learning algorithm implementation (e.g., data type), together with an efficiency assessment of this approach when the algorithm is tested on a set of circuit design benchmark functions.

Consequently, the over-fitting problem in the learning algorithm can be avoided [ 14, 15, 22].

The blind source separation (BSS) method proposed in 2001 by Belouchrani et al. (41) is a neural network based method that separates a linear mixture of stationary independent sources received by different sensors by the use of higher-order statistical moments in the learning algorithm.

The desired motion is generated in an empirical learning process by a learning algorithm and an intelligent structure, in which the learning algorithm adjusts the coordination levels of some primitive motions in order to generate the desired motion.

In addition, the learning algorithm is less sensitive to the selection of initial reproduction vectors.

As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters.

The theory of structural risk minimization reveals a trade-off between training error and classifier complexity in reducing generalization error, which will be exploited in the learning algorithms proposed in this paper.

Our primary contributions in this paper are (1) a formulation for modeling gait subspaces on the Grassmann manifold, (2) a framework to integrate supervised and unsupervised GE techniques in the Grassmann manifold, (3) a method to incorporate sparse representation in the learning algorithms, and (4) extensive experiment to corroborate the proposed approach.