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/493294/6399927

m baggingc.m

%BAGGINGC Bootstrapping and aggregation of classifiers % % W = BAGGINGC (A,CLASSF,N,ACLASSF,T) % % INPUT % A Training dataset. % CLASSF The base classifier (default: nmc) % N
·
www.eeworm.com/read/493294/6399931

m dd_roc.m

function [e, thr] = dd_roc(a,w) %DD_ROC Receiver Operating Characteristic curve % % E = DD_ROC(A,W) % E = DD_ROC(A*W) % E = A*W*DD_ROC % % Find for a (data description) method W
·
www.eeworm.com/read/493294/6399948

m knn_map.m

%KNN_MAP Map a dataset on a K-NN classifier % % F = KNN_MAP(A,W) % % INPUT % A Dataset % W k-NN classifier trained by KNNC % % OUTPUT % F Posterior probabilities % % DESCRIPTION % Maps t
·
www.eeworm.com/read/493294/6399951

m polyc.m

%POLYC Polynomial Classification % % W = polyc(A,CLASSF,N,S) % % INPUT % A Dataset % CLASSF Untrained classifier (optional; default: FISHERC) % N Degree of polynomial (optional;
·
www.eeworm.com/read/493294/6400010

m parsc.m

%PARSC Parse classifier % % PARSC(W) % % Displays the type and, for combining classifiers, the structure of the % mapping W. % % See also MAPPINGS % Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
·
www.eeworm.com/read/493294/6400014

m parallel.m

%PARALLEL Combining classifiers in different feature spaces % % WC = PARALLEL(W1,W2,W3, ....) or WC = [W1;W2;W3; ...] % WC = PARALLEL({W1;W2;W3; ...}) or WC = [{W1;W2;W3; ...}] % WC = PARALL
·
www.eeworm.com/read/493294/6400016

m dd_ex3.m

% DD_EX3 % % Show the use of the ksvdd: the support vector data description using % several different kernels. % % To be honest, the SVDD is the most useful using the RBF kernel. In % most case
·
www.eeworm.com/read/493294/6400239

m plotroc_update.m

function plotroc_update(w,a) % PLOTROC_UPDATE(W,A) % % Auxiliary function containing the callbacks for the plotroc.m. % % See also: plotroc % Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org % Faculty EW
·
www.eeworm.com/read/493294/6400243

m nmsc.m

%NMSC Nearest Mean Scaled Classifier % % W = NMSC(A) % % INPUT % A Trainign dataset % % OUTPUT % W Nearest Mean Scaled Classifier mapping % % DESCRIPTION % Computation of the linear discrim
·
www.eeworm.com/read/493294/6400248

m consistent_occ.m

function [w1,optval] = consistent_occ(x,w,fracrej,range,nrbags,varargin) %CONSISTENT_OCC % % W = CONSISTENT_OCC(X,W,FRACREJ,RANGE,NRBAGS) % % Optimize the hyperparameters of method W. W should con
·