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: 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: |
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Author:vera |
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Description: 机器学习领域经典分类算法综述，包括Decision Tree（ＩＤ３、C4.5(C5.0)、CART、PUBLIC、SLIQ和SPRINT算法），三种典型贝叶斯分类器（朴素贝叶斯算法、TAN算法、贝叶斯网络分类器），k-近邻 、 基于数据库技术的分类算法（ MIND算法、GAC-RDB算法），基于关联规则（CBA:Classification Based on Association Rule）的分类（Apriori算法），支持向量机分类，基于软计算的分类方法（粗糙集（rough set）、遗传算法、模糊逻辑、人工神经网络算法）。-Classical machine learning classification algorithms overview。 Including Decision Tree (ID3, C4.5 (C5.0), CART, PUBLIC, SLIQ and SPRINT algorithm), three typical Bayesian classifier (Naive Bayes algorithm, TAN algorithm Bayesian network classifiers), k-nearest neighbor based classification algorithm (MIND algorithm, GAC-RDB algorithms) database technology, based on association rules (CBA: classification (Apriori algorithm) Classification Based on Association Rule), and support vector machine classification, classification method based on soft computing (rough sets (rough set), genetic algorithms, fuzzy logic, artificial neural network algorithm).... Platform: |
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Author:MM |
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Description: 本书第一部分主要介绍机器学习基础，以及如何利用算法进行分类，并逐步介绍了多种经典的监督学习算法，如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树（CART）算法等。第三部分则重点介绍无监督学习及其一些主要算法：k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具。-
In the first part, mainly introduced the machine learning base, and how to use the algorithm to classify, and gradually introduced the variety of classical supervised learning algorithms such as k-nearest neighbor, naive Bayes algorithm, logistic regression algorithm, support vector machine (SVM) and AdaBoost ensemble method, based on the regression tree algorithm and classification and regression tree (CART) algorithm. The third part focuses on unsupervised learning and some of the main algorithms: K mean clustering algorithm, Apriori algorithm, FP-Growth algorithm. The fourth part introduces some tools of machine learning algorithm. Platform: |
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Author:孙伟 |
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