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/181388/9256604
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/181388/9256709
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/181388/9256715
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/362246/10010069
m contents.m
% Support Vector Machines.
%
% bsvm2 - Solver for multi-class BSVM with L2-soft margin.
% evalsvm - Trains and evaluates Support Vector Machines classifier.
% mvsvmclass - Majority votin
www.eeworm.com/read/357125/10215864
java lpknn.java
package mulan.classifier;
import java.util.HashSet;
import mulan.LabelSet;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.neighboursearch.LinearNNSearch;
/**
*
www.eeworm.com/read/280595/10311827
m contents.m
% Support Vector Machines.
%
% bsvm2 - Solver for multi-class BSVM with L2-soft margin.
% evalsvm - Trains and evaluates Support Vector Machines classifier.
% mvsvmclass - Majority votin
www.eeworm.com/read/161855/10360967
1 dbacl.1
\" t
.TH DBACL 1 "Bayesian Text Classification Tools" "Version 1.3" ""
.SH NAME
dbacl \- a digramic Bayesian classifier for text recognition.
.SH SYNOPSIS
.HP
.B dbacl
[-dvnirMND]
[-T
.IR type
] -l
www.eeworm.com/read/160517/10522530
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/351797/10609689
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/351797/10609856
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%