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 collectionclassifier2.java
// Working collection classifier - Page 130
import java.util.*;
public class CollectionClassifier2 {
public static String classify(Collection c) {
return (c instanceof Set ? "Set"
www.eeworm.com/read/179693/5302455
java collectionclassifier2.java
// Working collection classifier - Page 130
import java.util.*;
public class CollectionClassifier2 {
public static String classify(Collection c) {
return (c instanceof Set ? "Set"
www.eeworm.com/read/312185/3675467
java predictor.java
package jboost;
import jboost.booster.Prediction;
import jboost.examples.Instance;
import jboost.learner.IncompAttException;
/**
* An object that can classify Instances
*/
public in
www.eeworm.com/read/312185/3675544
java writablepredictor.java
package jboost;
import java.io.FileNotFoundException;
import java.io.IOException;
import jboost.examples.ExampleDescription;
/**
* An object that can classify Instances, and that can
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py classifier.py
# Description: Read data, build naive Bayesian classifier and classify first few instances
# Category: modelling
# Uses: voting.tab
# Referenced: c_basics.htm
import orange
data = or
www.eeworm.com/read/415311/11077016
m nearestneighborediting.m
function D = NearestNeighborEditing(train_features, train_targets, params, region)
% Classify points using the nearest neighbor editing algorithm
% Inputs:
% train_features - Train features
% t
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m perceptron_bvi.m
function D = Perceptron_BVI(train_features, train_targets, params, region)
% Classify using the batch variable increment Perceptron algorithm
% Inputs:
% features - Train features
% targets
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m perceptron_batch.m
function D = Perceptron_Batch(train_features, train_targets, params, region)
% Classify using the batch Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets
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m voted_perceptron.m
function D = voted_perceptron(train_features, train_targets, params, region);
% Classify using the Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Params
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m lms.m
function D = LMS(train_features, train_targets, params, region)
% Classify using the least means square algorithm
% Inputs:
% features - Train features
% targets - Train targets
% param -