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/441245/7673235
m klldc.m
%KLLDC Linear classifier built on the KL expansion of the common covariance matrix
%
% W = KLLDC(A,N)
% W = KLLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of significant eigenvectors
% AL
www.eeworm.com/read/441245/7673242
m tree_map.m
%TREE_MAP Map a dataset by binary decision tree
%
% F = TREE_MAP(A,W)
%
% INPUT
% A Dataset
% W Decision tree mapping
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset
www.eeworm.com/read/441245/7673387
m testn.m
%TESTN Error estimate of discriminant for normal distribution.
%
% E = TESTN(W,U,G,N)
%
% INPUT
% W Trained classifier mapping
% U C x K dataset with C class means, labels and priors (default
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/441245/7673407
m prtools.m
% Pattern Recognition Tools
% Version 4.1.4 11-Oct-2008
%
%Datasets and Mappings (just most important routines)
%---------------------
%dataset Define dataset from datamatrix and labels
%datasets
www.eeworm.com/read/436945/7758483
m classify.m
function [c, post] = classify(f, X);
%CLASSIFIER/CLASSIFY Categorise new data with CLASSIFIER object.
% [C, POST] = CLASSIFY(F, X) classifies the rows of the n by p
% feature matrix X given the CL
www.eeworm.com/read/387872/7849939
c count_dl.c
// Copyright (C) 2002-2003 Intel Corporation, All Rights Reserved.
// Permission is hereby granted to merge this program code with
// other program material to create a derivative work. This
www.eeworm.com/read/397122/8065856
m roc.m
function [AREA,SE,RESULT_S,FPR_ROC,TPR_ROC,TNa,TPa,FNa,FPa]=roc(RESULT,CLASS,fig)
% Receiver Operating Characteristic (ROC) curve of a binary classifier
%
% >> [area, se, deltab, oneMinusSpec, sen
www.eeworm.com/read/397111/8067056
m dd_fp.m
function e = dd_fp(w,z,err)
%DD_FP
%
% E = DD_FP(W,Z,ERR)
%
% Change the threshold of a (trained) classifier W, such that the error
% on the target class (the fraction false negative) is set to ERR
www.eeworm.com/read/397111/8067388
m p_map.m
%PARZEN_MAP Map a dataset on a Parzen densities based classifier
%
% F = p_map(A,W)
%
% Maps the dataset A by the Parzen density based classfier W. It
% outputs just the raw class probabilities (i.