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/255755/12057287
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.
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% OUTPUT
% W Combined classifier.
%
%
www.eeworm.com/read/255755/12057292
m rbnc.m
%RBNC Radial basis function neural network classifier
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% W = RBNC(A,UNITS)
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% INPUT
% A Dataset
% UNITS Number of RBF units in hidden layer
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% OUTPUT
% W Radial basis neural n
www.eeworm.com/read/255755/12057980
m costm.m
%COSTM Cost mapping, classification using costs
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% Y = COSTM(X,C,LABLIST)
% W = COSTM([],C,LABLIST)
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% DESCRIPTION
% Maps the classifier output X (assumed to be posterior probability
% estimate
www.eeworm.com/read/150905/12248242
m spatm.m
%SPATM Augment image dataset with spatial label information
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% E = SPATM(D,S)
% E = D*SPATM([],S)
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% INPUT
% D image dataset classified by a classifier
% S smoothing parameter
www.eeworm.com/read/150905/12248341
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/150905/12248347
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/150905/12249295
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/150760/12265718
m psvm.m
function varargout=psvm(model,options)
% PSVM Plots decision boundary of binary SVM classifier.
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% Synopsis:
% h = psvm(...)
% psvm(model)
% psvm(model,options)
%
% Description:
% This function s
www.eeworm.com/read/150760/12266108
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/149739/12352631
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