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/413912/11137089
m conffig.m
function fh=conffig(y, t)
%CONFFIG Display a confusion matrix.
%
% Description
% CONFFIG(Y, T) displays the confusion matrix and classification
% performance for the predictions mat{y} compared with
www.eeworm.com/read/411674/11233934
m andrerr.m
function [err,r,inx] = andrerr( model, distrib )
% ANDRERR Classification error of the Generalized Anderson's task.
%
% Synopsis:
% [err,r,inx] = andrerr( model, distrib )
%
% Description:
% This
www.eeworm.com/read/235011/14088934
txt sonar.txt
NAME: Sonar, Mines vs. Rocks
SUMMARY: This is the data set used by Gorman and Sejnowski in their study
of the classification of sonar signals using a neural network [1]. The
task is to train a netwo
www.eeworm.com/read/112466/15484750
tex class.tex
\rhead{class CLASSIFY}
\section{CLASSIFY : Contour Classification}
{\tt CLASSIFY} provides advance routines for detecting and classifying deformable contours directly from noisy image (Chapter 4 o
www.eeworm.com/read/111603/15509314
m svc.m
function net = svc(arg, sv, w, bias)
% SVC
%
% Construct a support vector classification (SVC) network object.
%
% Examples:
%
% % default constructor (linear, hardmargin SVC with no suppo
www.eeworm.com/read/111603/15509377
m pairwise.m
function net = pairwise(arg)
% PAIRWISE
%
% Construct a pairwise multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class pairwise network!)
%
www.eeworm.com/read/191902/8417331
m cart.m
function D = CART(train_features, train_targets, params, region)
% Classify using classification and regression trees
% Inputs:
% features - Train features
% targets - Train targets
% para
www.eeworm.com/read/386050/8768901
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/284357/8938248
m knn1.m
function [eachClass, ensembleClass, nearestSampleIndex, knnmat] = ...
knn(sampledata, testdata, k)
% KNN K-nearest neighbor rule for classification
% Usage:
% [EACH_CLASS, ENSEMBLE_CLASS, NEAREST
www.eeworm.com/read/282846/9056167
m getnoise_sp.m
function nmat = getnoise_sp(yeta,m)
% set up the "noise" matrix
%
% nmat = getnoise_sp(yeta,m)
% Matlab code for Gaussian Processes for Classification:
% GPCLASS vers