In this paper we consider exact tests of a multiple logistic regression, where the levels of covariates are equally spaced, via Markov beses. In usual application of multiple logistic regression, the sample size is positive for each combination of levels of the covariates. In this case we do not need a whole Markov basis, which guarantees connectivity of all fibers. We first give an explicit Ma...

Existing multiple kernel learning (MKL) algorithms indiscriminately apply a same set of kernel combination weights to all samples. However, the utility of base kernels could vary across samples and a base kernel useful for one sample could become noisy for another. In this case, rigidly applying a same set of kernel combination weights could adversely affect the learning performance. To improve...

A significant problem in gene expression analysis is that the sample size is substantially lower than the number of genes. Bagging is an effective method of solving this problem in the case of small sample datasets. We have devised a combination method, called the BagPART filtering method, that uses the projective adaptive resonance theory (PART) to select important genes and achieve a binary c...

Optimization of sample pre-treatment. Complete removal of cellular and further solid sample constituents, precipitation and removal of proteins, precipitation and removal of cholesterol from the blood sample and quantitative extraction of the analytes tryptophan, phenol, p-cresol and cholic acid in the diluted blood plasma phase. Step III: Combination and conversion of both isocratic HPLC/DAD/M...