代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
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java perceptronclassifier.java
package com.aliasi.classify;
import com.aliasi.corpus.ClassificationHandler;
import com.aliasi.corpus.Corpus;
import com.aliasi.matrix.DenseVector;
import com.aliasi.matrix.KernelFunction;
import co
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java clusterscoretest.java
package com.aliasi.test.unit.cluster;
import com.aliasi.classify.PrecisionRecallEvaluation;
import com.aliasi.cluster.ClusterScore;
import com.aliasi.test.unit.BaseTestCase;
import java.util.HashS
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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.
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m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
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m nearest_neighbor.m
function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_t
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m ada_boost.m
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
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m local_polynomial.m
function test_targets = Local_Polynomial(train_patterns, train_targets, test_patterns, Nlp)
% Classify using the local polynomial fitting
% Inputs:
% train_patterns - Train patterns
% train_tar
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asv ada_boost.asv
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
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m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
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m ada_boost.m
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets