Sentence examples for importance matrices from inspiring English sources
Sonnenburg et al. (2008) proposed positional oligomer importance matrices (POIMs) for WD kernels to analyze the importance of substrings in different locations of the sequence.
We will also display the Weight Plots and the Weight Mass for the weight matrices SVM- w in the same way as for the importance matrices.
We then calculate positional oligomer importance matrices developed by Sonnenburg et al. (2008) to obtain a 1mer sequence logo (Schneider and Stephens, 1990) from the kernel that shows the motif that this kernel is attuned to.
The results from the 10-fold CV have demonstrated the effectiveness of the k-mer selection of Round 1. Figure 3 shows two illustrative examples of importance matrices for k = 2 and k = 3.
Nevertheless, these importance matrices still provide an intuitive means for researchers to visualize and interpret results, and thus gain insights into the design of a binding sequence with a desired binding affinity.
Results: To make SVM-based sequence classifiers more accessible and profitable, we introduce the concept of positional oligomer importance matrices (POIMs) and propose an efficient algorithm for their computation.
Several properties of the importance matrix are discussed.
This new matrix is multiplied by the NFR priority vector obtained from the NFR importance matrix to obtain the overall objective (i.e., ranking of variants).
Combining the uncorrelated contributions and the correlated contribution components of all input variables, an importance matrix can be obtained to explicitly expose the contribution components of the correlated input variables to the variance of the output response.
An effective and simple SDP method in concept is further proposed to decompose the correlated contribution into the components, on which a second order importance matrix can be solved for explicitly exposing the contribution components of the correlated input variable to the variance of the output response.
The results also emphasized the importance of matrix interlock and non matrix aggregate void-filling capacity.
