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/386597/2570180
m click_points.m
function [patterns, targets, params, region] = click_points(region)
%Manually enter points into the workspace
ax = region(1:4);
h = findobj(findobj('Tag','classifier_GUI'),'Tag','txtNumberPo
www.eeworm.com/read/376881/2706661
m~ contents.m~
% Linear classifier based on the Fisher linear discriminat.
%
% fldqp - Computes Fisher's Linear Discriminat using QP.
% lfld - Learns Fisher's Linear Discriminat.
%
% About: Statistical Patt
www.eeworm.com/read/373460/2761815
c knnclass.c
/*---------------------------------------------------------------------------
[tst_labels] = knnclass(tst_data,trn_data,trn_labels, k)
KNNCLASS k-Nearest Neighbours classifier.
Input:
tst_data [dim
www.eeworm.com/read/373460/2761841
m bayesdemo1.m
% BAYESDEMO1 demo how to display discriminat function for Bayes classifier.
% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cm
www.eeworm.com/read/474600/6813508
m click_points.m
function [patterns, targets, params, region] = click_points(region)
%Manually enter points into the workspace
ax = region(1:4);
h = findobj(findobj('Tag','classifier_GUI'),'Tag','txtNumberPo
www.eeworm.com/read/367442/9747784
c knnclass.c
/*---------------------------------------------------------------------------
[tst_labels] = knnclass(tst_data,trn_data,trn_labels, k)
KNNCLASS k-Nearest Neighbours classifier.
Input:
tst_data [dim
www.eeworm.com/read/204456/15339313
m dlpdda.m
function W = dlpdda(x,nu,usematlab)
%DLPDDA Distance Linear Programming Data Description attracted by the Average distance
%
% W = DLPDDA(D,NU)
%
% This one-class classifier works directly on th
www.eeworm.com/read/190459/8443115
m deltablssvm.m
function model = deltablssvm(model,a1,a2)
% Bias term correction for the LS-SVM classifier
%
% >> model = deltablssvm(model, b_new)
%
% This function is only useful in the object oriented function
%
www.eeworm.com/read/431675/8661724
m perlc.m
%PERLC Linear classifier by linear perceptron
%
% W1 = perlc(A,n,step,w)
%
% Finds the linear discriminant function W1 (a mapping) by n cycles
% of the data through the linear perceptron with ste
www.eeworm.com/read/431675/8661878
m normal_map.m
%NORMAL_MAP Map a dataset on a normal densities based classifier
%
% F = normal_map(A,W)
%
% Maps the dataset A by the normal densities based classfier W on a
% [0,1] interval for each of the clas