The First Discriminant Theory of Linearly Separable Data
From Exams and Medical Diagnoses with Misclassifications to 169 Microarrays for Cancer Gene Diagnosis
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Focusing on the first discriminant theory of linearly separable data, the book presents Theory3, which builds on previous theories and utilizes 169 microarrays for analysis. It emphasizes the importance of accurate diagnoses by addressing misclassified patients within medical data. The author introduces RIP, an optimal-linear discriminant function designed to minimize misclassifications, showcasing its effectiveness in distinguishing between cases. This work aims to enhance the understanding and application of discriminant analysis in medical contexts.
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The First Discriminant Theory of Linearly Separable Data, Shuichi Shinmura
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- 2024
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- (Hardcover)
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