The First Discriminant Theory of Linearly Separable Data
From Exams and Medical Diagnoses with Misclassifications to 169 Microarrays for Cancer Gene Diagnosis
Autor*innen
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Parameter
- 380 Seiten
- 14 Lesestunden
Mehr zum Buch
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.
Buchkauf
The First Discriminant Theory of Linearly Separable Data, Shuichi Shinmura
- Sprache
- Erscheinungsdatum
- 2024
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- (Hardcover)
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- Titel
- The First Discriminant Theory of Linearly Separable Data
- Untertitel
- From Exams and Medical Diagnoses with Misclassifications to 169 Microarrays for Cancer Gene Diagnosis
- Sprache
- Englisch
- Autor*innen
- Shuichi Shinmura
- Erscheinungsdatum
- 2024
- Einband
- Hardcover
- Seitenzahl
- 380
- ISBN13
- 9789819994199
- Reihe
- Schlagwörter
- Sachbücher, Medizin & Gesundheit, Wissenschaft & Mathematik, Mathematik, Genetik
- Bewertung
- 5 von 5 Sternen
- Beschreibung
- 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.