代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/314653/13562199
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/402363/6343572
m svm.m
function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
% SVM - Create a Support Vector Machine classifier
%
% NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)
%
www.eeworm.com/read/493294/6399864
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/485544/6552731
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% per ro
www.eeworm.com/read/482915/6616175
m svm.m
function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
% SVM - Create a Support Vector Machine classifier
%
% NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)
%
www.eeworm.com/read/400577/11572566
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/260625/11716696
m svm.m
function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
% SVM - Create a Support Vector Machine classifier
%
% NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)
%
www.eeworm.com/read/255755/12057196
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/253950/12173580
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% per ro
www.eeworm.com/read/339665/12211571
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% per ro