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/222301/14697742
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/222301/14697749
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/213492/15133646
m psvm.m
function varargout=psvm(model,options)
% PSVM Plots decision boundary of binary SVM classifier.
%
% Synopsis:
% h = psvm(...)
% psvm(model)
% psvm(model,options)
%
% Description:
% This function s
www.eeworm.com/read/213492/15133789
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/213240/15140037
m incsvdd.m
function W = incsvdd(a,fracerr,ktype,par,kfunction)
%INCSVDD Incremental Support Vector Classifier
%
% W = INCSVDD(A,FRACERR,KTYPE,PAR)
%
% Use the incremental version of the SVDD. The kernel is d
www.eeworm.com/read/13871/284589
m demknn1.m
%DEMKNN1 Demonstrate nearest neighbour classifier.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with
www.eeworm.com/read/13911/286880
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/13911/286884
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/13911/287340
m pboundary.m
function varargout=pboundary(model,options)
% PBOUNDARY Plots decision boundary of given classifier in 2D.
%
% Synopsis:
% h = pboundary(model)
% h = pboundary(model,options)
%
% Description:
% Thi
www.eeworm.com/read/481713/1293600
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