Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a Variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble. Reference: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
標簽： Description Randomness Attribute Filtered
上傳時間： 2015-04-10
上傳用戶：ynzfm
Description: S-ISOMAP is a manifold learning algorithm, which is a supervised Variant of ISOMAP. Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
標簽： Description supervised algorithm S-ISOMAP
上傳時間： 2015-04-10
上傳用戶：wfeel
Variant code in this rar zipped package
標簽： Variant package zipped code
上傳時間： 2013-12-26
上傳用戶：shawvi
var matlab:Variant //通過"變體"調用接口是比較低效的,但很方便 begin //變體這種結構，本是vb中的東西。 try //如果已有活動的matlab.application對象,取其接口 matlab:=GetActiveOleObject( Matlab.Application ) except //這些個api所使用到的參數，其實都可以在注冊表里搜索到. matlab:=CreateOleObject( Matlab.Application ) //否則自己創建之 matlab:=CreateOleObject( Matlab.Application.5 ) matlab.execute( a=[1 1/ 3 1/5] ) //matlab.application接口具有 matlab.execute( b=[3 1 1/3] ) //這種方法(接口),否則會出錯 matlab.execute( plot(a,b) )；
上傳時間： 2013-12-18
上傳用戶：dapangxie
S-ISOMAP is a manifold learning algorithm, which is a supervised Variant of ISOMAP.
標簽： supervised algorithm S-ISOMAP manifold
上傳時間： 2014-01-11
上傳用戶：lacsx
NeC4.5 is a Variant of C4.5 decision tree, which could generate decision trees more accurate than standard C4.5 decision trees, through regarding a neural network ensemble as a pre-process of C4.5 decision tree.
標簽： decision 4.5 accurate generate
上傳時間： 2013-12-10
上傳用戶：anng
minimal python Variant for small footprint apps like embedded apps
標簽： apps footprint embedded minimal
上傳時間： 2013-12-16
上傳用戶：Andy123456
A sparse Variant of the Levenberg-Marquardt algorithm implemented by levmar has been applied to bundle adjustment, a computer vision/photogrammetry problem that typically involves several thousand variables
標簽： Levenberg-Marquardt implemented algorithm applied
上傳時間： 2013-12-23
上傳用戶：wqxstar
實現設備管理的主框架接口，接口采用Variant類型
上傳時間： 2016-03-25
上傳用戶：葉山豪
