Sentence examples for multiple regression algorithm from inspiring English sources

Support Vector Machine was used as multiple regression algorithm and modeling performance was evaluated by R2, RMSE and RPIQ.

Analysis entailed a two-step multiple regression algorithm, which was specifically designed to measure and separate load-sensitive and load-insensitive activity simultaneously, while preserving the original high spatial resolution of the fMRI signal.

Before data mining, the normalized microarray data was examined in a multiple regression algorithm to study chip-chip reproducibility (Additional file 1: Figure S4A).

Partial least squares regression is a multiple regression algorithm which attempts to explain the Y matrix by finding a multidimensional direction in the X space which explains the maximum variation in both matrices [ 10].

The network presented in Figure 2 is substantially smaller than that proposed by Gardner et al. (2003) Using their NIR (network identification by multiple regression) algorithm, they identified a network with 45 linkages (excluding changes due to MMC or the plasmids) compared with our network with only 13 gene linkages.

Support Vector Machines: Support Vector Machine implementation using the LibSVM library [23].... two workers do only support regression... Multiple Linear Regression: Multiple linear regression algorithm.

It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software.

The experimental planning to asses the influence of pressure, temperature and percentage of methanol and their interactions on the extraction efficiency was carried out with a factorial design, followed by multiple linear regression algorithm.

Missing data were estimated in SPSS v.16, within-sexes and within-OTUs, using a multiple linear regression algorithm.

Partial least squares (PLS) regression is a multiple linear regression algorithm which correlates variation in the Y matrix (cell survival) to the X matrix (phosphoprotein levels) by identifying vectors which simultaneously describe variation in both data sets.

Workspace requirements are considerably less than those of multiple-regression style algorithms, so the algorithm has great advantages particularly with large treatment models.