The First Discriminant Theory of Linearly Separable Data: From Exams and Medical Diagnoses with Misclassifications to 169 Microarrays for Cancer Gene
暫譯: 線性可分資料的首個判別理論：從考試和醫療診斷中的錯誤分類到169個癌症基因的微陣列

Shinmura, Shuichi

The First Discriminant Theory of Linearly Separable Data: From Exams and Medical Diagnoses with Misclassifications to 169 Microarrays for Cancer Gene
  • 出版商: Springer
  • 出版日期: 2025-05-01
  • 售價: $6,420
  • 貴賓價: 9.5 折 $6,099
  • 語言: 英文
  • 頁數: 347
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9819994225
  • ISBN-13: 9789819994229

    • 海外代購書籍(需單獨結帳)

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商品描述

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 Method2 (Program3), that splits microarray into many SMs. Program4 can split microarray into many BGSs. Then, the wide column LSD (Revolution-0), such as microarray (n

Theory3 shows the surprising results of six ordinary data re-analyzed by Theory1 and Theory2 knowledge. Essence of Theory3 is described by using cephalopelvic disproportion (CPD) data. RIP discriminates CPD data (240*19) and finds two misclassifications unique for cesarean and natural-born groups. CPD238 omitting two patients becomes LSD, which is the first case selection method. Program4 finds BGS (14 vars.) the only variable selection method for Theory3. 32 (=25) models, including BGS, become LSD among (219-1) models. Because Program2 confirms BGS has the minimum average error rate, BGS is the most compact and best model satisfying Occam's Razor.

With this book, physicians obtain complete diagnostic results for disease, and engineers can become a true data scientist, by obtaining integral knowledge of statistics and mathematical programming with simple programs.

商品描述(中文翻譯)

本書探討了線性可分資料的第一個判別理論（LSD），即理論3，基於理論1的四個普通LSD和理論2的169個微陣列（LSD）。此外，您可以快速分析醫療數據，並處理誤分類的病人，這是診斷的真正目的。作者在數十年前開發了RIP（最佳線性判別函數尋找組合最佳解），該方法找到了最小的誤分類。RIP對瑞士鈔票（200*6）進行了63個（=26-1）模型的判別，並找到了最小的LSD：基本基因集（BGS）。在理論2中，RIP對Shipp微陣列（77*7129）進行了判別，該微陣列是LSD，並且只有32個非零係數（第一個小俄羅斯套娃；SM1）。因為RIP判別了另外7,097個基因並找到了SM2，作者開發了俄羅斯套娃特徵選擇方法2（Program3），該方法將微陣列拆分成多個SM。Program4可以將微陣列拆分成多個BGS。然後，寬列LSD（Revolution-0），例如微陣列（n）

理論3顯示了六個普通數據經過理論1和理論2知識重新分析的驚人結果。理論3的本質是通過使用頭盆不稱（CPD）數據來描述的。RIP對CPD數據（240*19）進行了判別，並找到了對剖腹產和自然出生組獨特的兩個誤分類。省略兩名病人的CPD238變成了LSD，這是第一個案例選擇方法。Program4找到了BGS（14個變數），是理論3的唯一變數選擇方法。32個（=25）模型，包括BGS，在（219-1）模型中成為LSD。因為Program2確認BGS具有最低的平均錯誤率，BGS是最緊湊且滿足奧卡姆剃刀的最佳模型。

通過本書，醫生可以獲得完整的疾病診斷結果，而工程師則可以成為真正的數據科學家，通過簡單的程序獲得統計學和數學編程的整體知識。

作者簡介

Shuichi Shinmura is Emeritus Professor in Seikei University, Tokyo. His publication includes "High-dimensional Microarray Data Analysis: Cancer Gene Diagnosis and Malignancy Indexes by Microarray" (Springer Nature 2019) and "New Theory of Discriminant Analysis After R. Fisher: Advanced Research by the Feature Selection Method for Microarray Data" (Springer 2017).

作者簡介(中文翻譯)

新村修一是東京成蹊大學的名譽教授。他的著作包括《高維度微陣列數據分析：癌症基因診斷與微陣列的惡性指數》（Springer Nature 2019）和《R. Fisher之後的判別分析新理論：基於特徵選擇方法的微陣列數據進階研究》（Springer 2017）。

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