Description: Programs to induce a naive possibilistic classifier (a possibilistic analog of the naive Bayes classifier) and to classify new data with an induced naive possibilistic classifier.-Programs to induce a naive possibilistic c lassifier (a possibilistic analog of the naive Bayesian classifier) and to classify new data with an induced naive possibilistic classifier. Platform: |
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Author:无心 |
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Description: Naive Bayes 分类器, 用C语言写的.大家一起参考一下. -Naive Bayes classifier, using the C language. U.S. with reference. Platform: |
Size: 5120 |
Author:sj2ac |
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Description: naive bayes方法是用于分类的一个传统方法，它是产生的比较早的一个方法，分类的准确度比较高。-naive bayes method is used for the classification of a traditional method, which is generated as early as one way of comparison, classification accuracy is relatively high. Platform: |
Size: 1024 |
Author:李萍 |
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Description: 这是一个matlab 开发的naive Bayes 分类器算法。可以供大家共享，修改和批评指正。这个是修改的别人的。如果有务，联系：
l_y_f_2005@163.com-This is a matlab developed algorithm naive Bayes classifier. Be available for everyone to share, modify, and criticism. This is a modification of someone else Platform: |
Size: 1024 |
Author:122456 |
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Description: 朴素贝叶斯分类器（Naive Bayes Classifier），可以分类任意属性个数和目标状态的离散样例。-Naive Bayesian classifier (Naive Bayes Classifier), can be categorized arbitrary number of attributes and objectives of the state of the discrete sample. Platform: |
Size: 608256 |
Author:王军 |
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Description: 贝叶斯分类算法，构造朴素贝叶斯分类器，对给定的中文文本进行分类-Bayesian classification algorithm, Naive Bayes classifier structure of a given Chinese text classification Platform: |
Size: 2048 |
Author:娜娜 |
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Description: 实现朴素贝叶斯文本分类器，用java实验，还包括readme和训练集-Naive Bayes text classifier to achieve with java experiments, including readme and the training set Platform: |
Size: 3046400 |
Author:wangdong |
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Description: this program is used for classification of noisy pixel identification by using naive bayes classifier Platform: |
Size: 12288 |
Author:RajBiradar |
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Description: Naive Bayes classifier
Input: Training set and testing set, each row represents a instance, last column is label, begins from zero
Output:predict label by Naive Bayes as well as its accuracy Platform: |
Size: 1024 |
Author:Anthony |
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Description: 对朴素贝叶斯算法的进一步改进。朴素贝叶斯分类器是一种简单而高效的分类器，但是它的属性独立性假设使其无法表示现实世界属性之间的依赖关系，以及它的被动学习策略，影响了它的分类性能。本文从不同的角度出发，讨论并分析了三种改进朴素贝叶斯分类性能的方法。为进一步的研究打下坚实的基础-Naive Bayes algorithm further improved. Naive Bayes classifier is a simple and efficient classifier, but its attribute independence assumption it can not be said that the dependencies between the properties of the real world, as well as the passive learning strategies, affect its classification performance. Starting from different points of view to discuss and analyze the three improved the performance of the naive Bayesian classifier. To lay a solid foundation for further study Platform: |
Size: 159744 |
Author:xujingxue |
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Description: 程序实现了naive bayes classifier， 并附有对美国参议院根据投票情况来判断议员属于民主党还是共和党的例子。-Program achieved a naive bayes classifier, along with the U.S. Senate voting to determine under Democrat and Republican members belonging examples. Platform: |
Size: 4096 |
Author:杜晗 |
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Description: 使用Python实现朴素贝叶斯分类，文件夹中附带数据集。实现了NB算法，并进行5倍交叉验证-Naive Bayes classifier using the Python implementation, the folder with the data set. NB implements the algorithm, and 5-fold cross-validation Platform: |
Size: 521216 |
Author:刘韵 |
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Description: 本文以拼写检查作为例子，讲解Naive Bayes分类器是如何实现的。对于用户输入的一个单词（words），拼写检查试图推断出最有可能的那个正确单词（correct）。当然，输入的单词有可能本身就是正确的。比如，输入的单词thew，用户有可能是想输入the，也有可能是想输入thaw。为了解决这个问题，Naive Bayes分类器采用了后验概率P(c|w)来解决这个问题。P(c|w)表示在发生了w的情况下推断出c的概率。为了找出最有可能c，应找出有最大值的P(c|w)，即求解问题-In this paper, spell check as an example to explain the Naive Bayes classifier is implemented. For a user to enter a word (words), the spelling checker tries to infer the most likely the correct word (correct). Of course, it is possible to enter the word itself is correct. For example, enter the word thew, users may want to enter the, there may be trying to enter the thaw. After To solve this problem, Naive Bayes classifier using a posterior probability P (c | w) to solve this problem. The P (c | w) represents the case of the probability of w c is inferred. In order to identify the most likely c, you should find out the maximum value of P (c | w), that is, to solve the problem Platform: |
Size: 1024 |
Author:王志坦 |
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