代码搜索：Nearest

找到约 1,596 项符合「Nearest」的源代码

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m knnclass.m

function y = knnclass(X,model) % KNNCLASS k-Nearest Neighbours classifier. % % Synopsis: % y = knnclass(X,model) % % Description: % The input feature vectors X are classified using the K-NN % rule
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www.eeworm.com/read/113576/15453092

m svmclassnpa.m

function [xsup,alpha,b,pos]=svmclassnpa(x,y,C,kernel,kerneloption,verbose); % USAGE % [xsup,alpha,b,pos]=svmclassnpa(x,y,C,kernel,kerneloption,verbose); % % % Main ROUTINE For Nearest P
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www.eeworm.com/read/389962/8490877

m nn.m

%{ NN - Creates forecasts of a time series on t+1 using nearest neighbour algorithm. Usage: [OutSample_For,InSample_For,InSample_Res]=nn(x,d,m,k,method,n) INPUT:
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www.eeworm.com/read/375962/9341342

c kdtree.c

/* Functions and structures for maintaining a k-d tree database of image features. For more information, refer to: Beis, J. S. and Lowe, D. G. Shape indexing using approximate nearest
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www.eeworm.com/read/375212/9369111

m cluster.m

function cluster(dat,labels,fig00) %CLUSTER KNN and K-means cluster analysis with dendrograms % This function performs a cluster analysis using either % the K Nearest Neighbor (KNN) or K-means clus
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www.eeworm.com/read/373632/9445405

contents

Entry: knn.var Aliases: knn.var Keywords: models Description: K-Nearest Neighbor Classification With Variable Selection URL: ../../../library/knnTree/html/knn.var.html Entry: knnTree Aliases:
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www.eeworm.com/read/362500/9996163

m cluster.m

function cluster(dat,labels,fig00) %CLUSTER KNN and K-means cluster analysis with dendrograms % This function performs a cluster analysis using either % the K Nearest Neighbor (KNN) or K-means cl
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www.eeworm.com/read/166631/10010634

m lle.m

% LLE ALGORITHM (using K nearest neighbors) % % [Y] = lle(X,K,dmax) % % X = data as D x N matrix (D = dimensionality, N = #points) % K = number of neighbors % dmax = max embedding dimensionality % Y =
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www.eeworm.com/read/360732/10080582

m ltsa.m

% ltsa - local tangent planes alignement % % [T,NI] = ltsa(X,d,K); % % X is the (d,n) n data points in R^d. % d is the output dimensionnality. % K is the number of nearest neighbors. %
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www.eeworm.com/read/281020/10272154

m nn.m

%{ NN - Creates forecasts of a time series on t+1 using nearest neighbour algorithm. Usage: [OutSample_For,InSample_For,InSample_Res]=nn(x,d,m,k,method,n) INPUT:
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