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/293183/8310811
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
www.eeworm.com/read/367442/9747825
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/367442/9748266
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/411674/11233683
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/411674/11233916
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/204456/15339332
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/111603/15509316
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/111603/15509320
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/289680/8534976
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a support vector classifier network using the specified tutor.
%
% load data/iris x y;
%
% C = 100;
% kernel = r
www.eeworm.com/read/188280/8552112
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a support vector classifier network using the specified tutor.
%
% load data/iris x y;
%
% C = 100;
% kernel = r