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/128684/5980325
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/493294/6399859
m spatm.m
%SPATM Augment image dataset with spatial label information
%
% E = SPATM(D,S)
% E = D*SPATM([],S)
%
% INPUT
% D image dataset classified by a classifier
% S smoothing parameter
www.eeworm.com/read/493294/6399933
m averagec.m
%AVERAGEC Combining of linear classifiers by averaging coefficients
%
% W = AVERAGEC(V)
% W = V*AVERAGEC
%
% INPUT
% V A set of affine base classifiers.
%
% OUTPUT
% W Combined classifier.
%
%
www.eeworm.com/read/493294/6399935
m rbnc.m
%RBNC Radial basis function neural network classifier
%
% W = RBNC(A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of RBF units in hidden layer
%
% OUTPUT
% W Radial basis neural n
www.eeworm.com/read/493294/6400309
m costm.m
%COSTM Cost mapping, classification using costs
%
% Y = COSTM(X,C,LABLIST)
% W = COSTM([],C,LABLIST)
%
% DESCRIPTION
% Maps the classifier output X (assumed to be posterior probability
% estimate
www.eeworm.com/read/493294/6400339
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/492400/6422295
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/483114/6609666
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/483114/6609670
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/480105/6676778
c gentleboost_model.c
/*
Gentle AdaBoost Classifier with two different weak-learners : Decision Stump and Perceptron.
Multi-class problem is performed with the one-against-all strategy.
Usage
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