代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
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java cglinearclassifierfactory.java
package edu.stanford.nlp.classify;
import java.util.*;
import edu.stanford.nlp.util.*;
import edu.stanford.nlp.optimization.*;
/** @author Dan Klein */
public class CGLinearClassifierFactory exten
www.eeworm.com/read/199528/5076123
java maxentclassifierfactory.java
package edu.stanford.nlp.classify;
import java.util.*;
import edu.stanford.nlp.util.*;
import edu.stanford.nlp.optimization.*;
/** @author Dan Klein */
public class MaxentClassifierFactory extends
www.eeworm.com/read/199528/5076124
java classifierexample.java
package edu.stanford.nlp.classify;
import edu.stanford.nlp.dbm.*;
import java.util.*;
public class ClassifierExample {
protected static String GREEN = "green";
protected static String RED = "r
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1 file.1
.th FILE I 1/16/75
.sh NAME
file \*- determine file type
.sh SYNOPSIS
.bd file
file ...
.sh DESCRIPTION
.it File
performs a series of tests on each argument
in an attempt to classify it.
If an argumen
www.eeworm.com/read/386597/2570096
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I
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m multivariate_splines.m
function test_targets = Multivariate_Splines(train_patterns, train_targets, test_patterns, params)
% Classify using multivariate adaptive regression splines
% Inputs:
% train_patterns - Train pa
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m perceptron_batch.m
function [test_targets, a, updates] = Perceptron_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using the batch Perceptron algorithm
% Inputs:
% train_patterns - Train pa
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m perceptron_voted.m
function test_targets = Perceptron_Voted(train_patterns, train_targets, test_patterns, params)
% Classify using the Voted Perceptron algorithm
% Inputs:
% train_patterns - Train patterns
% trai
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m optimal_brain_surgeon.m
function [test_targets, Wh, Wo, J] = Optimal_Brain_Surgeon(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and remov
www.eeworm.com/read/386597/2570137
m relaxation_bm.m
function [test_targets, a] = Relaxation_BM(train_patterns, train_targets, test_patterns, params)
% Classify using the batch relaxation with margin algorithm
% Inputs:
% train_patterns - Train pa