代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/485544/6552717
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 prio
www.eeworm.com/read/483114/6609667
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/264146/11327610
m one_error.m
function OneError=One_error(Outputs,test_target)
%Computing the one error
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Outputs(j,i
www.eeworm.com/read/407916/11408590
cpp haarfeatures.cpp
/*
* This file is part of MultiBoost, a multi-class
* AdaBoost learner/classifier
*
* Copyright (C) 2005-2006 Norman Casagrande
* For informations write to nova77@gmail.com
*
* This library is free
www.eeworm.com/read/400577/11572649
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer
% S Standard deviation of weights in an input layer (default: 1
www.eeworm.com/read/400577/11573003
m mogc.m
%MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
%
% INPUT
% A Dataset
% N Number of mixtures (optional; default 2)
% R,S Regularization parameters, 0
www.eeworm.com/read/400577/11573200
m lssvc.m
function W = lssvc(A, TYPE, PAR, C)
%LSSVC Least-Squares Support Vector Classifier
%
% W = lssvc(A,TYPE,PAR,C);
%
% INPUT
% A dataset
% TYPE Type of the kernel (optional; default: '
www.eeworm.com/read/400577/11573256
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/400576/11573476
m dlpdd.m
function W = dlpdd(x,nu,usematlab)
%DLPDD Distance Linear Programming Data Description
%
% W = DLPDD(D,NU)
%
% This one-class classifier works directly on the distance (dissimilarity)
% matrix
www.eeworm.com/read/342008/12047691
m testd.m
%TESTD Classification error estimate
%
% [e,j,k,l] = testd(A,W,r,iter)
%
% Test of dataset A on the classifier defined by W. Returns:
% e - the fraction of A that is incorrectly classified by W.
%