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/351797/10609866
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/273047/10930336
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/418695/10935160
m medianc.m
%MEDIANC Median combining classifier
%
% W = medianc(V)
% W = V*medianc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the median combiner: it selects
www.eeworm.com/read/418695/10935239
m prodc.m
%PRODC Product combining classifier
%
% W = prodc(V)
% W = V*prodc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the product combiner: it selects the
www.eeworm.com/read/417741/10977087
java naivebayes.java
package ir.classifiers;
import java.io.*;
import java.util.*;
import ir.vsr.*;
import ir.utilities.*;
/**
* Implements the NaiveBayes Classifier with Laplace smoothing. Stores probabilities
* inte
www.eeworm.com/read/299984/7140330
m lmnc.m
%LMNC Levenberg-Marquardt trained feed-forward neural net classifier
%
% [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each hid
www.eeworm.com/read/299984/7140547
m testc.m
%TESTC Test classifier, error / performance estimation
%
% [E,C] = TESTC(A*W,TYPE)
% [E,C] = TESTC(A,W,TYPE)
% E = A*W*TESTC([],TYPE)
%
% [E,F] = TESTC(A*W,TYPE,LABEL)
% [E,F] = TESTC(A,
www.eeworm.com/read/299984/7140570
m mapping.m
%MAPPING Mapping class constructor
%
% W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, SIZE_IN, SIZE_OUT)
%
% A map/classifier object is constructed. It may be used to map a dataset A
% on anoth
www.eeworm.com/read/460435/7250805
m lmnc.m
%LMNC Levenberg-Marquardt trained feed-forward neural net classifier
%
% [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each hid
www.eeworm.com/read/460435/7251023
m testc.m
%TESTC Test classifier, error / performance estimation
%
% [E,C] = TESTC(A*W,TYPE)
% [E,C] = TESTC(A,W,TYPE)
% E = A*W*TESTC([],TYPE)
%
% [E,F] = TESTC(A*W,TYPE,LABEL)
% [E,F] = TESTC(A,