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/397102/8068262
m roc.m
%ROC Receiver-operator curve
%
% e = roc(D,k)
%
% Computes k points of the receiver-operator curve of the classifier
% W for the labeled data set D, which is typically the result of
% D = A*W*clas
www.eeworm.com/read/397102/8068359
m reject.m
%REJECT Compute error-reject trade-off curve
%
% e = reject(D)
%
% Computes the error-reject curve of the classification result
% D = A*W, in which A is a dataset and W a classifier. e is a
% set
www.eeworm.com/read/143706/12850025
m train_test_multiple_class_al.m
function run = train_test_multiple_class_AL(X, Y, trainindex, testindex, classifier)
global preprocess;
% The statistics of dataset
num_class = length(preprocess.ClassSet);
actual_num_class =
www.eeworm.com/read/312163/13617451
m knnclass.m
function y = knnclass(X,model)
% KNNCLASS k-Nearest Neighbours classifier.
%
% Synopsis:
% y = knnclass(X,model)
%
% Description:
% The input feature vectors X are classified using the K-NN
% rule
www.eeworm.com/read/310621/13648608
m singleweaklearner.m
function [H]=SingleWeakLearner(X,Y,C,W)
% Train a weak classifier wrt ONE feature given in C
% Use 2-class Gaussian model:
%
% Input
% X - samples
% Y - label of samples -
% 1
www.eeworm.com/read/135153/5889778
c cls_u32.c
/*
* net/sched/cls_u32.c Ugly (or Universal) 32bit key Packet Classifier.
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public
www.eeworm.com/read/493294/6399995
m dd_normc.m
%DD_NORMC Normalize the output of a oc-classifier
%
% B = DD_NORMC(A)
% B = A*W*DD_NORMC
% W = DD_NORMC
%
% Normalize the mapped dataset A to standard 'posterior probability'
% est
www.eeworm.com/read/492400/6422204
m lpdd.m
%LPDD Linear programming distance data description
%
% W = LPDD(X,NU,S,DTYPE,P)
%
% One-class classifier put into a linear programming framework. From
% the data X the distance matrix is comp
www.eeworm.com/read/492400/6422250
m dd_normc.m
%DD_NORMC Normalize the output of a oc-classifier
%
% B = DD_NORMC(A)
% B = A*W*DD_NORMC
% W = DD_NORMC
%
% Normalize the mapped dataset A to standard 'posterior probability'
% est
www.eeworm.com/read/400577/11572647
m svc.m
%SVC Support Vector Classifier
%
% [W,J] = SVC(A,KERNEL,C)
% [W,J] = SVC(A,TYPE,PAR,C)
% W = A*SVC([],KERNEL,C)
% W = A*SVC([],TYPE,PAR,C)
%
% INPUT
% A Dataset
% KERNEL - Un