標(biāo)題: Titlebook: New Theory of Discriminant Analysis After R. Fisher; Advanced Research by Shuichi Shinmura Book 2016 Springer Science+Business Media Singap [打印本頁] 作者: 稀少 時間: 2025-3-21 16:29
書目名稱New Theory of Discriminant Analysis After R. Fisher影響因子(影響力)
書目名稱New Theory of Discriminant Analysis After R. Fisher影響因子(影響力)學(xué)科排名
書目名稱New Theory of Discriminant Analysis After R. Fisher網(wǎng)絡(luò)公開度
書目名稱New Theory of Discriminant Analysis After R. Fisher網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱New Theory of Discriminant Analysis After R. Fisher被引頻次
書目名稱New Theory of Discriminant Analysis After R. Fisher被引頻次學(xué)科排名
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書目名稱New Theory of Discriminant Analysis After R. Fisher年度引用學(xué)科排名
書目名稱New Theory of Discriminant Analysis After R. Fisher讀者反饋
書目名稱New Theory of Discriminant Analysis After R. Fisher讀者反饋學(xué)科排名
作者: Yourself 時間: 2025-3-21 21:02 作者: Missile 時間: 2025-3-22 03:07 作者: 憤怒事實(shí) 時間: 2025-3-22 07:43
rse matrices (Problem 3)..For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5). If we call the linearly separable model "Matroska," the dataset consi978-981-10-9546-7978-981-10-2164-0作者: 松雞 時間: 2025-3-22 11:17
New Theory of Discriminant Analysis,ient. Because there are finite CPs in the discriminant coefficient space, we should select the CP interior point with MNM. We call this CP, “optimal CP (OCP).” MNM decreases monotonously (MNM.?≥?NMN.). Therefore, if MNM.?=?0, all MNMs of the models, including these .-variables, are zero. If data are作者: 相容 時間: 2025-3-22 14:28 作者: CREST 時間: 2025-3-22 20:54 作者: cognizant 時間: 2025-3-23 00:26 作者: 收集 時間: 2025-3-23 03:04 作者: Delude 時間: 2025-3-23 07:20 作者: Countermand 時間: 2025-3-23 13:06 作者: 冒失 時間: 2025-3-23 15:11
Matroska Feature-Selection Method for Microarray Dataset (Method 2),. The Method 1 offers a 95?% CI for the error rate and coefficient. We obtained two means of the error rates, M1 and M2, in the training and validation samples and proposed a simple model selection procedure to choose the best model with a minimum M2. We compared two statistical LDFs and six MP-base作者: 新娘 時間: 2025-3-23 21:07 作者: infringe 時間: 2025-3-23 23:55
Book 2016d discriminate LSD theoretically (Problem 2). We solved the defect of the generalized inverse matrices (Problem 3)..For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5). If we call the linearly separable model "Matroska," the dataset consi作者: Goblet-Cells 時間: 2025-3-24 06:02 作者: Ruptured-Disk 時間: 2025-3-24 06:41
New Theory of Discriminant Analysis After R. Fisher978-981-10-2164-0作者: 消耗 時間: 2025-3-24 14:16
New Theory of Discriminant Analysis,ely solve these problems through five mathematical programming-based linear discriminant functions (MP-based LDFs). First, I develop an optimal linear discriminant function using integer programming (IP-OLDF) based on a minimum number of misclassifications (minimum NM (MNM)) criterion. We consider d作者: Intrepid 時間: 2025-3-24 16:51
,Iris Data and Fisher’s Assumption,s. Because Fisher evaluates Fisher’s LDF with these data, such data are very popular for the evaluation of discriminant functions. Therefore, we call these data, “Fisher’s Iris data.” Because we can easily separate setosa from virginica and vercicolor through a scatter plot, we usually discriminate 作者: AGGER 時間: 2025-3-24 20:00 作者: 船員 時間: 2025-3-25 01:11 作者: exorbitant 時間: 2025-3-25 03:45 作者: 調(diào)整 時間: 2025-3-25 11:22 作者: 軍火 時間: 2025-3-25 15:27
Japanese-Automobile Data, seats (.3), CO. (.4), fuel (.4), and sales (.6). The following points are important for this book: (1) LSD discrimination: We can easily recognize that these data are LSD because .1 and .3 can separate two classes completely by two box–whisker plots. (2) Problem 3: The forward stepwise procedure se作者: hemophilia 時間: 2025-3-25 17:53 作者: 恃強(qiáng)凌弱 時間: 2025-3-25 20:37
LINGO Program 2 of Method 1,ate and discriminant coefficient. Therefore, we proposed the 100-fold cross-validation for small sample method (the Method 1). The Method 1 is the combination of resampling and k-fold cross-validation. We generate large sample as validation sample by resampling and undertake 100-fold cross-validatio作者: 縮減了 時間: 2025-3-26 00:52 作者: cajole 時間: 2025-3-26 05:15
Shuichi Shinmura Schichtenstr?mungen erweitert.NEU: CD-ROM "Aufgaben zur Str.Dieses erfolgreiche Lehrbuch stellt die Str?mungslehre als einheitliche Wissenschaft dar, die in allen Zweigen den gemeinsamen Prinzipien der Kontinuumsmechanik folgt. Einzeldisziplinen der Str?mungslehre werden nach dem Grundgesetz "vom A作者: Obliterate 時間: 2025-3-26 09:19 作者: 廣口瓶 時間: 2025-3-26 15:27
Shuichi ShinmuraCompares eight LDFs by seven different kinds of data sets from the points of view of M2 and 95% CI of the coefficient.Presents solutions for five serious problems of discriminant analysis and finds im作者: 直覺沒有 時間: 2025-3-26 18:31
http://image.papertrans.cn/n/image/665851.jpg作者: 軍火 時間: 2025-3-27 00:08
https://doi.org/10.1007/978-981-10-2164-0Comparison of Eight LDFs; Model Selection by Best Model; 100-fold Cross Validation for Small Sample Me作者: 提名的名單 時間: 2025-3-27 02:15 作者: 直覺沒有 時間: 2025-3-27 08:34 作者: Hallmark 時間: 2025-3-27 10:12
Book 2016ote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed the 100-fold cross-validation for the small sampl作者: Deference 時間: 2025-3-27 15:04
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