代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/455967/7360594
m sublargesvc.m
function [nsv, alpha, b0] = sublargesvc(X,Y,ker,C)
%SVC Support Vector Classification
%
% Usage: [nsv alpha bias] = svc(X,Y,ker,C)
%
% Parameters: X - Training inputs
% Y - Tr
www.eeworm.com/read/455967/7360595
asv sublargesvc.asv
function [nsv, alpha, b0] = sublargesvc(X,Y,ker,C)
%SVC Support Vector Classification
%
% Usage: [nsv alpha bias] = svc(X,Y,ker,C)
%
% Parameters: X - Training inputs
% Y - Tr
www.eeworm.com/read/450608/7480390
m roc.m
%ROC Receiver-Operator Curve
%
% E = ROC(A,W,C,N)
% E = ROC(B,C,N)
%
% INPUT
% A Dataset
% W Trained classifier, or
% B Classification result, B = A*W*CLASSC
% C Index of desired clas
www.eeworm.com/read/441245/7673023
m roc.m
%ROC Receiver-Operator Curve
%
% E = ROC(A,W,C,N)
% E = ROC(B,C,N)
%
% INPUT
% A Dataset
% W Trained classifier, or
% B Classification result, B = A*W*CLASSC
% C Index of desired clas
www.eeworm.com/read/440460/7689030
m plotbpboundary.m
function PlotBpBoundary(W,iter,style)
% PlotBpBoundary Plot classification boundary based on weight matrix W.
NUNITS = size(W,1);
colors = get(gca,'ColorOrder');
ncolors = size(colors,1);
c1 = [1
www.eeworm.com/read/299244/7870502
readme
Type Classification Code:
main.m (program control)
discretize.m (converts image to discrete values)
plotimg.m (plots images)
dirImg.m (computes the directional image)
extract.m (extract
www.eeworm.com/read/398337/7993655
m nfcv.m
function [xapp,yapp,xtest,ytest,indice]=nfcv(x,y,N,k,classcode)
% USAGE
% [xapp,yapp,xtest,ytest]=nfcv(x,y,N,k)
% this is for classification with output code as -1 1
% so that the prior prob of
www.eeworm.com/read/143733/12848013
m plotbpboundary.m
function PlotBpBoundary(W,iter,style)
% PlotBpBoundary Plot classification boundary based on weight matrix W.
NUNITS = size(W,1);
colors = get(gca,'ColorOrder');
ncolors = size(colors,1);
c1 = [1
www.eeworm.com/read/137160/13342265
m roc.m
%ROC Receiver-Operator Curve
%
% E = ROC(A,W,C,N)
% E = ROC(B,C,N)
%
% INPUT
% A Dataset
% W Trained classifier, or
% B Classification result, B = A*W*CLASSC
% C Index of desired clas
www.eeworm.com/read/317012/13512291
m svcm_train.m
function [a, b, g, inds, inde, indw] = svcm_train(x, y, C);
% function [a, b, g, inds, inde, indw] = svcm_train(x, y, C);
% support vector classification machine
% incremental learning,