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/483891/6597172
gcc makefile.gcc
# Compiler
# CC = gcc
# Flags
#OPTFLAGS = -march=i686 -mcpu=i686 -malign-double -funroll-loops -O3
OPTFLAGS = -O3
CFLAGS = $(OPTFLAGS)
# Libraries
L_ARCH = $(ARCH)
LIB_NAME = d-$(L_ARCH).a
F2C
www.eeworm.com/read/482720/6621674
m osusvmdemo.m
% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations---
%
% 1) Demonstrations of using C-SVM Classifers.
% 2) Demonstrations of using u-SVM Classifiers
% 3) Demonstration
www.eeworm.com/read/400577/11572622
m perlc.m
% PERLC - Train a linear perceptron classifier
%
% W = PERLC(A)
% W = PERLC(A,MAXITER,ETA,W_INI,TYPE)
%
% INPUT
% A Training dataset
% MAXITER Maximum number of iterations (default 10
www.eeworm.com/read/400577/11573343
m logdens.m
%LOGDENS Force density based classifiers to use log-densities
%
% V = LOGDENS(W)
% V = W*LOGDENS
%
% INPUT
% W Density based trained classifier
%
% OUTPUT
% V Log-density based tr
www.eeworm.com/read/157718/11670239
m osusvmdemo.m
% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations---
%
% 1) Demonstrations of using C-SVM Classifers.
% 2) Demonstrations of using u-SVM Classifiers
% 3) Demonstration
www.eeworm.com/read/344640/11869948
m osusvmdemo.m
% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations---
%
% 1) Demonstrations of using C-SVM Classifers.
% 2) Demonstrations of using u-SVM Classifiers
% 3) Demonstration
www.eeworm.com/read/344640/11869996
m osusvmdemo.m
% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations---
%
% 1) Demonstrations of using C-SVM Classifers.
% 2) Demonstrations of using u-SVM Classifiers
% 3) Demonstration
www.eeworm.com/read/342711/12005235
m cerror.m
function error=cerror(y1,y2,label)
% CERROR Computes classification error.
%
% Synopsis:
% error = cerror(y1,y2)
% error = cerror(y1,y2,label)
%
% Description:
% error = cerror(y1,y2) returns clas
www.eeworm.com/read/342711/12005260
asv cerror.asv
function error=cerror(y1,y2,label)
% CERROR Computes classification error.
%
% Synopsis:
% error = cerror(y1,y2)
% error = cerror(y1,y2,label)
%
% Description:
% error = cerror(y1,y2) returns clas
www.eeworm.com/read/342008/12046849
m udc.m
%UDC Uncorrelated normal based quadratic Bayes classifier
%
% W = udc(A)
%
% Computation a quadratic classifier between the classes in the
% dataset A assuming normal densities with uncorrelated f