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/451547/7462017
m optim_auc.m
function [w,optval] = optim_auc(x,wname,fracrej,range,nrbags,varargin)
%OPTIM_AUC Optimize hyperparameters for an OCC
%
% W = OPTIM_AUC(X,WNAME,FRACREJ,RANGE,NRBAGS,VARARGIN)
%
% Optimize the AUC-p
www.eeworm.com/read/451308/7467554
java cvlearningcurve.java
package ir.classifiers;
import java.io.*;
import java.util.*;
import ir.vsr.*;
import ir.utilities.*;
/**
* Gives learning curves with K-fold cross validation for a classifier.
*
* @author
www.eeworm.com/read/450608/7480073
m medianc.m
%MEDIANC Median combining classifier
%
% W = MEDIANC(V)
% W = V*MEDIANC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Median combining classifier on V
%
% DESCRIPTION
% If V = [V
www.eeworm.com/read/450608/7480126
m classc.m
%CLASSC Convert mapping to classifier
%
% W = CLASSC(W)
% W = W*CLASSC
%
% INPUT
% W Any mapping or dataset
%
% OUTPUT
% W Classifier mapping or normalized dataset: outputs/features sum to 1
%
www.eeworm.com/read/450608/7480135
m prodc.m
%PRODC Product combining classifier
%
% W = PRODC(V)
% W = V*PRODC
%
% INPUT
% V Set of classifiers trained on the same classes
%
% OUTPUT
% W Product combiner
%
% DESCRIPTION
% It def
www.eeworm.com/read/450608/7480143
m contents.m
% Pattern Recognition Tools
% Version 4.0.14 04-Mar-2005
%
%Datasets and Mappings (just most important routines)
%---------------------
%dataset Define and retrieve dataset from datamatrix and lab
www.eeworm.com/read/450608/7480226
m contents.m
% Pattern Recognition Tools
% Version URV 24-Mar-2004
%
% This is prelimanary, many support routines in ./private ./@datasets
% and ./@mappings are not mentioned.
%
%Datasets and Mappings (just most i
www.eeworm.com/read/450608/7480345
m prtools.m
% Pattern Recognition Tools
% Version URV 24-Mar-2004
%
% This is prelimanary, many support routines in ./private ./@datasets
% and ./@mappings are not mentioned.
%
%Datasets and Mappings (just most i
www.eeworm.com/read/450608/7480394
m meanc.m
%MEANC Mean combining classifier
%
% W = MEANC(V)
% W = V*MEANC
%
% INPUT
% V Set of classifiers (optional)
%
% OUTPUT
% W Mean combiner
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a s
www.eeworm.com/read/450608/7480416
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% W = LDC(A,R,S)
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0