代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/415311/11077084
m contents.m
% Classification GUI and toolbox
% Version 1.0
%
% GUI start commands
%
% classifier - Start the classification GUI
% enter_distributions - Starts the parameter input screen (used by classif
www.eeworm.com/read/111603/15509315
m display.m
function display(net)
% DISPLAY
%
% Display a textual representation of a support vector classifier object.
%
% display(net);
%
% File : @svc/display.m
%
% Date : Wednesd
www.eeworm.com/read/450549/7481640
h resource.h
//{{NO_DEPENDENCIES}}
// Microsoft Developer Studio generated include file.
// Used by Classifier.rc
//
#define IDM_ABOUTBOX 0x0010
#define IDD_ABOUTBOX 100
www.eeworm.com/read/431675/8661769
m polyc.m
%POLYC Polynomial Classification
%
% W = polyc(A,classf,n,s)
%
% Adds polynomial features to the dataset A and runs the untrained
% classifier classf. n is the degree of the polynome (default 1).
www.eeworm.com/read/431675/8661832
m kljlc.m
%KLJLC Linear classifier using KL expansion on the joint data.
%
% W = kljlc(A,n)
%
% Finds the linear discriminant function W for the dataset A
% computing the ldc on a projection of the data on
www.eeworm.com/read/386050/8768160
m parzendc.m
%PARZENDC Parzen density based classifier
%
% [W,H] = PARZENDC(A)
% W = PARZENDC(A,H)
%
% INPUT
% A Dataset
% H Smoothing parameters (optional; default: estimated from A for each class)
www.eeworm.com/read/386050/8768299
m naivebc.m
%NAIVEBC Naive Bayes classifier
%
% W = NAIVEBC(A,N)
% W = A*NAIVEBC([],N)
%
% W = NAIVEBC(A,DENSMAP)
% W = A*NAIVEBC([],DENSMAP)
%
% INPUT
% A Training dataset
% N Scalar numbe
www.eeworm.com/read/386050/8769701
m prex_plotc.m
%PREX_PLOTC PRTools example on the dataset scatter and classifier plot
help prex_plotc
echo on
% Generate Higleyman data
A = gendath([100 100]);
% Split the data into the
www.eeworm.com/read/284728/8905791
txt 支持向量机源代码.txt
>>edit svmtrain
>>edit svmclassify
>>edit svmpredict
function [svm_struct, svIndex] = svmtrain(training, groupnames, varargin)
%SVMTRAIN trains a support vector machine classifier
%
% SV
www.eeworm.com/read/365739/9849765
m coverage.m
function Coverage=coverage(Outputs,test_target)
%Computing the coverage
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Outputs(j,i)