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/140853/13058146
m u_rbfdemo.m
echo off
% RBFDEMO demonstration for using nonlinear SVM classifier
% with a RBF kernel.
echo on;
clc
% RBFDEMO demonstration for using nonlinear SVM classifier
% with a RBF kernel.
%#####
www.eeworm.com/read/140853/13058150
m u_lindemo.m
echo off
%LINDEMO demonstration for using linear SVM classifier.
echo on;
clc
%LINDEMO demonstration for using linear SVM classifier.
%#########################################################
www.eeworm.com/read/140853/13058153
m c_clademo.m
echo off
% CLADEMO demonstration for using a contructed SVM classifier to classify
% input patterns
echo on;
%
%
% NOTICE: please first run any of the first three demonstrations before
%
www.eeworm.com/read/140853/13058197
m svmclass.m
function [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias, Parameters, nSV, nLabel)
% Usages:
% [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias);
% [Labels, DecisionValu
www.eeworm.com/read/139485/13154222
c bci.c
/*----------------------------------------------------------------------
File : bci.c
Contents: naive and full Bayes classifier induction
Author : Christian Borgelt
History : 08.12.1998 fi
www.eeworm.com/read/139485/13154224
c bcdb.c
/*----------------------------------------------------------------------
File : bcdb.c
Contents: generate a database from a Bayes classifier
Author : Christian Borgelt
History : 26.04.2003
www.eeworm.com/read/139485/13154261
c bcx.c
/*----------------------------------------------------------------------
File : bcx.c
Contents: naive and full Bayes classifier execution
Author : Christian Borgelt
History : 08.12.1998 fi
www.eeworm.com/read/137160/13341851
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/137160/13341881
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/137160/13341883
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;