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/441245/7672664
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/441245/7672667
m rbnc.m
%RBNC Radial basis function neural network classifier
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% W = RBNC(A,UNITS)
%
% 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/441245/7673224
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/299459/7850252
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/299459/7850784
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/398324/7994114
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/398324/7994127
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/398324/7994223
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/398324/7994237
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/397102/8068474
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