Description: ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN Platform: |
Size: 1631348 |
Author:辉腾 |
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Description: 一个由Mike Gashler完成的机器学习方面的includes neural net, naive bayesian classifier, decision tree, KNN, a genetic algorithm, and some manifold learning algorithms. -by Mike Gashler a complete machine learning includes the neur al net, naive bayesian classifier. decision tree, KNN, a genetic algorithm, manifold and some learning algorithms. Platform: |
Size: 1625088 |
Author:lyb |
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Description: ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN -ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN Platform: |
Size: 1631232 |
Author:辉腾 |
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Description: 朴素贝叶斯(Naive Bayes, NB)算法是机器学习领域中常用的一种基于概率的分类算法，非常简单有效。k近邻法(k-Nearest Neighbor, kNN)[30,31]又称为基于实例(Example-based, Instance-bases)的算法，其基本思想相当直观：Rocchio法来源于信息检索系统，后来最早由Hull在1994年应用于分类[74]，从那以后，Rocchio方法就在文本分类中广泛应用起来。-Naive Bayes (Naive Bayes, NB) algorithm is commonly used in the field of machine learning a classification algorithm based on probability is very simple and effective. k neighbor method (k-Nearest Neighbor, kNN) [30,31], also known as case-based (Example-based, Instance-bases) of the algorithm, the basic idea quite intuitive: Rocchio law from the Information Retrieval System, and later was first proposed by the Hull in 1994, applies to classification [74], since then, Rocchio method of classification in the text with extensive application. Platform: |
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Author:许梁 |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification, Platform: |
Size: 622592 |
Author:sukmawati |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification Platform: |
Size: 635904 |
Author:ParisM |
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Description: Bayes分类器——算法设计
1． 使用决策树(Decision tree)分类算法、朴素贝叶斯（Naï ve Bayes）算法或者K-近邻（kNN）算法（三者任选其一）对给定的训练数据集构造分类器，并在测试数据集上进行分类预测。
2． 数据集描述：
Tic-tac-toe游戏的二叉分类。Tic-tac-toe游戏示例如下-Bayes classifier- Algorithm 1. Using the decision tree (Decision tree) classification algorithm, Naive Bayes (Naï ve Bayes) algorithm or K-nearest neighbor (kNN) algorithm (choose any one of three) on a given set of training data classification structure, and the test data Classification and Prediction on the set. 2. Data set description: Tic-tac-toe game binary classification. Tic-tac-toe game example is as follows Platform: |
Size: 1439744 |
Author:vera |
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Description: （经典聚类算法）
国际权威的学术组织the IEEE International Conference on Data Mining (ICDM) 2006年12月评选出了数据挖掘领域的十大经典算法：C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
不仅仅是选中的十大算法，其实参加评选的18种算法，实际上随便拿出一种来都可以称得上是经典算法，它们在数据挖掘领域都产生了极为深远的影响。-(Classical clustering algorithm)
International authoritative academic organization of the IEEE International Conference on Data Mining (ICDM) in December 2006 selected the top ten of the field of data mining algorithm: the C4.5, k-Means, SVM, of Apriori, the EM, the PageRank, AdaBoost, kNN , the Naive Bayes, and the CART.
Not just the selected 10 algorithms, in fact, participate in the selection of 18 kinds of algorithms, in fact, easily come up with one can be called a classical algorithm in the field of data mining, they have had far-reaching impact. Platform: |
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Author:赵鑫维 |
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Description: 讲述了最著名的十大数据挖掘算法，经典资料，国际权威的学术组织the IEEE International Conference on Data Mining (ICDM) 2006年12月评选出了数据挖掘领域的十大经典算法：C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.-About the top ten most famous data mining algorithms, the classical information, the international authority of the academic organization of the IEEE International Conference on Data Mining (ICDM) 2006, selected the top ten of the field of data mining algorithms: the C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Platform: |
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Author:吴贵锋 |
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Description: 介绍数据挖掘的10种主要算法及其应用 一种透过数理模式来分析企业内储存的大量资料，以找出不同的客户或市场划分，分析出消费者喜好和行为的方法。 -Top 10 algorithms in data mining his paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5,k-Means, SVM,
Apriori, EM, PageRank, AdaBoost,kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community. With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
review current and further research on the algorithm. These 10 algorithms cover classification, Platform: |
Size: 633856 |
Author:andyzygg |
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Description: 采用有监督的朴素贝叶斯、SVM和KNN算法对进行训练，实现对邮件的分类-Using supervised naive bayes, SVM and KNN algorithm for training, implementation of the classification of the mail Platform: |
Size: 4472832 |
Author:马敬超 |
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Search - KNN and naive bayes