代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

代码结果 4,824
热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/204456/15339290

m dd_normc.m

%DD_NORMC Normalize the output of a oc-classifier % % B = DD_NORMC(A) % B = A*W*DD_NORMC % W = DD_NORMC % % Normalize the mapped dataset A to standard 'posterior probability' % est
·
www.eeworm.com/read/289680/8535008

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
·
www.eeworm.com/read/289680/8535152

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
·
www.eeworm.com/read/289680/8535159

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a dag-svm multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
·
www.eeworm.com/read/188280/8552154

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
·
www.eeworm.com/read/188280/8552292

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
·
www.eeworm.com/read/188280/8552307

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a dag-svm multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
·
www.eeworm.com/read/431675/8661680

m medianc.m

%MEDIANC Median combining classifier % % W = medianc(V) % W = V*medianc % % If V = [V1,V2,V3, ... ] is a set of classifiers trained on the % same classes and W is the median combiner: it selects
·
www.eeworm.com/read/431675/8661824

m prodc.m

%PRODC Product combining classifier % % W = prodc(V) % W = V*prodc % % If V = [V1,V2,V3, ... ] is a set of classifiers trained on the % same classes and W is the product combiner: it selects the
·
www.eeworm.com/read/386050/8768166

m lmnc.m

%LMNC Levenberg-Marquardt trained feed-forward neural net classifier % % [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T) % % INPUT % A Dataset % UNITS Array indicating number of units in each hid
·