Sentence examples for matrices trained with from inspiring English sources

We analysed the E. coli K-12 Mgenome(NC_000913) genome using pre-defined parameters (see User Guide) and matrices trained with data from ten promoter boxes and twenty one rho-independent terminators [21] of experimentally verified E. coli K-12 sRNA genes.

To the first problem, we use the transfer learning technology to transfer the weights of a projection matrix trained with strict image prior constraint to one convolution layer.

Besides, we will see in the next section that the localization errors of the five wind sequences among the test set of 1995 or among the training set of1975 94, on the smoothed matrix trained with 1975 94, are similar (see Table 7), indicating that this matrix represents both periods.

Since the weights of the matrix are trained with strict image prior constraint, the convolution layer whose parameters are transferred from the well-trained matrix has took the image prior into account.

The weight matrix was trained with a self-organizing map algorithm in the Finnish SOM Toolbox (http://www.cis.hut.fi/projects/somtoolbox/) using a melody that systematically moved through all the major and minor keys in Western tonal music (Janata, Birk, et al. 2002; Janata et al. 2003).

To measure this, we look at the rank of the weight matrices of networks trained with soft targets.

For ab initio gene finding, predictions of protein-coding regions were carried out using GeneMark.hmm and Genescan programs with the matrix trained by an A. thaliana gene set, and predictions of exon intron structure were performed using NetGene2 and SplicePredictor programs.

A graphical example for the alpha and beta PVs is provided in Figure 5. Genetic algorithms were trained with the matrix describing the presence/absence of TFBS in the URR of the papillomaviruses.

Then these vectors are trained with Non-negative matrix factorization algorithm to find part-based bases.

The key structure of the naive Bayes fusion kernel in this model is the post probability matrix for individual classifiers that we trained with labeled samples.

Coding regions in non-annotated genomic sequences were identified using FGENESH [ 58] trained with the appropriate genome matrix.