代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/450608/7480574
m prtestc.m
%PRTESTC Test routine for the PRTOOLS classifier
%
% This script tests a given, untrained classifier w, defined in the
% workspace, e.g. w = my_classifier. The goal is to find out whether
% w fulfill
www.eeworm.com/read/441245/7672804
m setcost.m
%SETCOST Reset classification cost matrix of dataset
%
% A = SETCOST(A,COST,LABLIST)
%
% The classification cost matrix of the dataset A is reset to COST.
% COST should have size [C,C+n], n >= 0, if
www.eeworm.com/read/441245/7673256
m reject.m
%REJECT Compute the error-reject trade-off curve
%
% E = REJECT(D);
% E = REJECT(A,W);
%
% INPUT
% D Classification result, D = A*W
% A Dataset
% W Cell array of trained classifiers
www.eeworm.com/read/441245/7673292
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/441245/7673401
m prtestc.m
%PRTESTC Test routine for the PRTOOLS classifier
%
% This script tests a given, untrained classifier w, defined in the
% workspace, e.g. w = my_classifier. The goal is to find out whether
% w fulfill
www.eeworm.com/read/441015/7677934
m parzenpnnlearn.m
function net = parzenPNNlearn(samples,classification,center)
% PARZENPNNLEARN Creates a Parzen probabilistic neural network
%
% This funcion generates a Parzen PNN (Probabilistic Neural Network) fro
www.eeworm.com/read/441015/7677941
m parzenpnnimprove.m
function neto = parzenPNNimprove(net,samples,classification)
% PARZENPNNLEARN Creates a Parzen probabilistic neural network
%
% This funcion improves a Parzen PNN (Probabilistic Neural Network) from
www.eeworm.com/read/245632/12786764
readme
BSVM:
*****************************************************************
COPYRIGHT NOTIFICATION
BSVM can be freely used for research purpose.
Use for commercial purposes is expressly proh
www.eeworm.com/read/137160/13342020
m setcost.m
%SETCOST Reset classification cost matrix of dataset
%
% A = SETCOST(A,COST,LABLIST)
%
% The classification cost matrix of the dataset A is reset to COST.
% COST should have size [C,C+n], n >= 0, if
www.eeworm.com/read/137160/13342376
m reject.m
%REJECT Compute the error-reject trade-off curve
%
% E = REJECT(D);
% E = REJECT(A,W);
%
% INPUT
% D Classification result, D = A*W
% A Dataset
% W Cell array of trained classifiers