Il libro è attualmente esaurito
Maggiori informazioni sul libro
The book uniquely compares eight linear discriminant functions (LDFs) across various datasets, including Fisher's iris data and medical data with collinearities. It introduces a 100-fold cross-validation method tailored for small samples and presents a straightforward model selection procedure to identify the optimal model based on minimum M2. The Revised IP-OLDF, evaluated using the MNM criterion, demonstrates superior performance compared to other M2s across the examined datasets, making it a significant contribution to statistical modeling and data analysis.
Acquisto del libro
New Theory of Discriminant Analysis After R. Fisher, Shuichi Shinmura
- Lingua
- Pubblicato
- 2017
- product-detail.submit-box.info.binding
- (Copertina rigida)
Ti avviseremo via email non appena lo rintracceremo.
Metodi di pagamento
Ancora nessuna valutazione.
- Sottotitolo
- Advanced Research by the Feature Selection Method for Microarray Data
- Lingua
- Inglese
- Autori
- Shuichi Shinmura
- Editore
- Springer
- Pubblicato
- 2017
- Formato
- Copertina rigida
- Pagine
- 228
- ISBN13
- 9789811021633
- Serie
- Descrizione
- The book uniquely compares eight linear discriminant functions (LDFs) across various datasets, including Fisher's iris data and medical data with collinearities. It introduces a 100-fold cross-validation method tailored for small samples and presents a straightforward model selection procedure to identify the optimal model based on minimum M2. The Revised IP-OLDF, evaluated using the MNM criterion, demonstrates superior performance compared to other M2s across the examined datasets, making it a significant contribution to statistical modeling and data analysis.