The First Discriminant Theory of Linearly Separable Data

线性可分数据的*判别理论:从错误分类的检查与医学诊断到169个用于癌症基因诊断的微阵列

数学史

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作      者
出  版 社
出版时间
2024年02月17日
装      帧
精装
ISBN
9789819994199
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页      码
359
语      种
英文
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图书简介
This book deals with the first discriminant theory of linearly separable data (LSD), Theory3, based on the four ordinary LSD of Theory1 and 169 microarrays (LSD) of Theory2. Furthermore, you can quickly analyze the medical data with the misclassified patients which is the true purpose of diagnoses. Author developed RIP (Optimal-linear discriminant function finding the combinatorial optimal solution) as Theory1 in decades ago, that found the minimum misclassifications. RIP discriminated 63 (=26−1) models of Swiss banknote (200*6) and found the minimum LSD: basic gene set (BGS). In Theory2, RIP discriminated Shipp microarray (77*7129) which was LSD and had only 32 nonzero coefficients (first Small Matryoshka; SM1). Because RIP discriminated another 7,097 genes and found SM2, the author developed the Matryoshka feature selection Method 2 (Program 3), that splits microarray into many SMs. Program4 can split microarray into many BGSs. Then, the wide column LSD (Revolution-0), such as microarray (n
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