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/280595/10312282
m perceptron.m
function model=perceptron(data,options,init_model)
% PERCEPTRON Perceptron algorithm to train binary linear classifier.
%
% Synopsis:
% model = perceptron(data)
% model = perceptron(data,options)
%
www.eeworm.com/read/161855/10360896
readme
DBACL - digramic Bayesian classifier
PURPOSE
dbacl is a command line program which can be used to categorize
several types of text documents. Each document category is
constructed as a maximum ent
www.eeworm.com/read/159921/10587840
m pbayescln.m
function pbayescln(MI,SIGMA,Pk,background, linestyle)
% PBAYESCLN vizualizes Bayes classifier discriminant in 2D.
% pbayescln(MI,SIGMA,Pk,background, linestyle )
%
% This fucntion vizualizes discrimi
www.eeworm.com/read/159921/10588575
m fishdemo.m
function []=fishdemo(action,hfigure,varargin)
% FISHDEMO demo on algorithms which learn Fisher's classifer.
%
% FISHDEMO demonstrates use of algorithms finding the Fisher's
% classifier. The task is
www.eeworm.com/read/351797/10609642
m getsv.m
function sv = getsv(net)
% GETSV
%
% Accessor method returning the support vectors of a support vector
% classifier network.
%
% sv = getsv(net);
%
% File : @svc/getsv.m
%
% D
www.eeworm.com/read/351797/10609659
m getw.m
function w = getw(net)
% GETW
%
% Accessor method returning the weights of a support vector classifier network.
%
% w = getw(net);
%
% File : @svc/getw.m
%
% Date : Tuesd
www.eeworm.com/read/421949/10676525
m pbayescln.m
function pbayescln(MI,SIGMA,Pk,background, linestyle)
% PBAYESCLN vizualizes Bayes classifier discriminant in 2D.
% pbayescln(MI,SIGMA,Pk,background, linestyle )
%
% This fucntion vizualizes discrimi
www.eeworm.com/read/421949/10677265
m fishdemo.m
function []=fishdemo(action,hfigure,varargin)
% FISHDEMO demo on algorithms which learn Fisher's classifer.
%
% FISHDEMO demonstrates use of algorithms finding the Fisher's
% classifier. The task is
www.eeworm.com/read/418695/10935583
m clevalf.m
%CLEVALF Classifier evaluation (feature size curve)
%
% [e,s] = clevalf(classf,A,featsizes,learnsize,n,T,print)
%
% Generates at random for all feature sizes stored in featsizes
% training sets of
www.eeworm.com/read/418695/10935620
m emclust.m
%EMCLUST Expectation - Maximization clustering
%
% [D,V] = emclust(A,W,n)
%
% The untrained classifier W is used to update an initially labelled
% dataset A by the following two steps:
% 1. train W by