代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/382446/2636747
java classifierevaluatortest.java
package com.aliasi.test.unit.classify;
import com.aliasi.test.unit.BaseTestCase;
import com.aliasi.classify.Classification;
import com.aliasi.classify.ConditionalClassification;
import com.aliasi.cl
www.eeworm.com/read/382446/2636750
java knnclassifiertest.java
package com.aliasi.test.unit.classify;
import com.aliasi.classify.KnnClassifier;
import com.aliasi.classify.Classification;
import com.aliasi.classify.Classifier;
import com.aliasi.classify.ScoredCla
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java bernoulliclassifiertest.java
package com.aliasi.test.unit.classify;
import com.aliasi.classify.BernoulliClassifier;
import com.aliasi.classify.Classification;
import com.aliasi.classify.Classifier;
import com.aliasi.classify.Sco
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java svmlightclassificationparsertest.java
package com.aliasi.test.unit.corpus.parsers;
import com.aliasi.test.unit.BaseTestCase;
import com.aliasi.classify.Classification;
import com.aliasi.corpus.ClassificationHandler;
import com.aliasi.
www.eeworm.com/read/160391/5571188
m hme_class_plot.m
function fh=hme_class_plot(net, nodes_info, train_data, test_data)
%
% Use this function ONLY when the input dimension is 2
% and the problem is a classification one.
% We assume that each row of
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m learn_params.m
function CPD = learn_params(CPD, fam, data, ns, cnodes, varargin)
% LEARN_PARAMS Construct classification/regression tree given complete data
% CPD = learn_params(CPD, fam, data, ns, cnodes)
%
% f
www.eeworm.com/read/471358/6890719
m svc.m
function [nsv, alpha, b0] = svc(X,Y,ker,C)
%SVC Support Vector Classification
%
% Usage: [nsv alpha bias] = svc(X,Y,ker,C)
%
% Parameters: X - Training inputs
% Y - Training t
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m svc.m
function [nsv, alpha, b0] = svc(X,Y,ker,C)
%SVC Support Vector Classification
%
% Usage: [nsv alpha bias] = svc(X,Y,ker,C)
%
% Parameters: X - Training inputs
% Y - Training t
www.eeworm.com/read/294886/8195750
m nnd10lc.m
function nnd10lc(cmd,arg1,arg2,arg3)
%NND10LC Linear pattern classification demonstration.
% First Version, 8-31-95.
%==================================================================
% CON
www.eeworm.com/read/294863/8197244
m svc.m
function [nsv, alpha, b0] = svc(X,Y,ker,C)
%SVC Support Vector Classification
%
% Usage: [nsv alpha bias] = svc(X,Y,ker,C)
%
% Parameters: X - Training inputs
% Y - Training t