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/253950/12174198
m confmat.m
function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the prediction
www.eeworm.com/read/253950/12174215
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of which
www.eeworm.com/read/339991/12188671
m svcinfo.m
function svcinfo(trn,tst,ker,alpha,bias)
%SVCINFO Support Vector Classification Results
%
% Usage: svcinfo(trn,tst,ker,alpha,bias)
%
% Parameters: trn - Training set
% tst - Test
www.eeworm.com/read/339665/12211825
m confmat.m
function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the prediction
www.eeworm.com/read/339665/12211871
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of which
www.eeworm.com/read/253585/12213077
m char3.m
%% Character Recognition Example (III):Training a Simple NN for
%% classification
%% Read the image
I = imread('sample.bmp');
%% Image Preprocessing
img = edu_imgpreprocess(I);
for cnt = 1:5
www.eeworm.com/read/150905/12249342
m fdsc.m
%FDSC Feature based Dissimilarity Space Classification
%
% W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF)
% W = A*FDSC([],R,FEATMAP,TYPE,P,CLASSF)
%
% INPUT
% A Dateset used for training
% R
www.eeworm.com/read/150905/12250612
m confmat.m
function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the prediction
www.eeworm.com/read/150905/12250645
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of which
www.eeworm.com/read/149739/12353634
m fdsc.m
%FDSC Feature based Dissimilarity Space Classification
%
% W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF)
% W = A*FDSC([],R,FEATMAP,TYPE,P,CLASSF)
%
% INPUT
% A Dateset used for training
% R