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/397106/8067811
m perceptron_vccore.m
% Learns classifier and classifies test set
% using the perceptron learning algorithm
% Works with 2 class labels, any number of features
% when the class labels are 0 and 1.
% Invoke using Percep
www.eeworm.com/read/397102/8068685
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.
%
www.eeworm.com/read/245176/12813162
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/143706/12849758
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/143706/12850058
m train_test_multiple_class.m
% Input pararmeter:
% D: data array, including the feature data and output class
function run = train_test_multiple_class(X, Y, trainindex, testindex, classifier)
global preprocess;
% The s
www.eeworm.com/read/143706/12850113
m train_test_multiple_label.m
% Input pararmeter:
% D: data array, including the feature data and output class
function run = train_test_multiple_label(X, Y, trainindex, testindex, classifier)
global preprocess;
% The s
www.eeworm.com/read/140850/13059490
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/137160/13341890
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 (default: 10)
% S Standard deviation of weights in an input lay
www.eeworm.com/read/137160/13342446
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/320830/13417569
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