代码搜索：classifiers
找到约 2,305 项符合「classifiers」的源代码
代码结果 2,305
www.eeworm.com/read/400576/11573519
readme
Data Description Matlab toolbox. (version 1.7.0)
This toolbox is an add-on to the PRTools toolbox. The toolbox contains
algorithms to train, investigate, visualize and evaluate one-class
classifiers
www.eeworm.com/read/213240/15139956
m isocc.m
%ISOCC True for one-class classifiers
%
% isocc(w) returns true if the classifier w is a one-class classifier,
% outputting only classes 'target' and/or 'outlier' and having a
% structure with thr
www.eeworm.com/read/213240/15140009
readme
Data Description Matlab toolbox. (version 1.5.7)
This toolbox is an add-on to the PRTools toolbox. The toolbox contains
algorithms to train, investigate, visualize and evaluate one-class
classifiers
www.eeworm.com/read/436207/1850416
howto-svmlight-weka
1) Create a 'sparse' directory in weka/classifiers and put SVMlight.java there.
2) Look at the directories for SVMlight binaries and temporary files which are
defined on lines 65 and 67 in SVMligh
www.eeworm.com/read/436207/1850771
entries
D/associations////
D/attributeSelection////
D/classifiers////
D/clusterers////
D/core////
D/datagenerators////
D/estimators////
D/experiment////
D/filters////
D/gui////
D/deduping////
D/extraction////
www.eeworm.com/read/204456/15339253
m isocc.m
%ISOCC True for one-class classifiers
%
% isocc(w) returns true if the classifier w is a one-class classifier,
% outputting only classes 'target' and/or 'outlier' and having a
% structure with thr
www.eeworm.com/read/204456/15339306
readme
Data Description Matlab toolbox. (version 1.5.5)
This toolbox is an add-on to the PRTools toolbox. The toolbox contains
algorithms to train, investigate, visualize and evaluate one-class
classifiers
www.eeworm.com/read/431675/8662249
m mclassc.m
%MCLASSC Computation of multi-class classifier from 2-class discriminants
%
% W = mclassc(A,classf)
%
% The untrained classifier classf is called to compute c classifiers
% between each of the c class
www.eeworm.com/read/386050/8767611
m normal_map.m
%NORMAL_MAP Map a dataset on normal-density classifiers or mappings
%
% F = NORMAL_MAP(A,W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% F Density estimation for classes in A
%
% DESC
www.eeworm.com/read/357125/10215875
java multilabelclassifier.java
package mulan.classifier;
import weka.classifiers.Classifier;
import weka.core.Instance;
import weka.core.Instances;
public interface MultiLabelClassifier
{
public int getNumLabels();