Titlebook: New Theory of Discriminant Analysis After R. Fisher; Advanced Research by Shuichi Shinmura Book 2016 Springer Science+Business Media Singap

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Matroska Feature-Selection Method for Microarray Dataset (Method 2),. The Method 1 offers a 95?% CI for the error rate and coefficient. We obtained two means of the error rates, M1 and M2, in the training and validation samples and proposed a simple model selection procedure to choose the best model with a minimum M2. We compared two statistical LDFs and six MP-base

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Book 2016d discriminate LSD theoretically (Problem 2). We solved the defect of the generalized inverse matrices (Problem 3)..For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5). If we call the linearly separable model "Matroska," the dataset consi

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New Theory of Discriminant Analysis After R. Fisher978-981-10-2164-0

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New Theory of Discriminant Analysis,ely solve these problems through five mathematical programming-based linear discriminant functions (MP-based LDFs). First, I develop an optimal linear discriminant function using integer programming (IP-OLDF) based on a minimum number of misclassifications (minimum NM (MNM)) criterion. We consider d

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,Iris Data and Fisher’s Assumption,s. Because Fisher evaluates Fisher’s LDF with these data, such data are very popular for the evaluation of discriminant functions. Therefore, we call these data, “Fisher’s Iris data.” Because we can easily separate setosa from virginica and vercicolor through a scatter plot, we usually discriminate

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