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Description: pop3代理服务器源代码One of the most powerful features of Pop3 Agent is a naive Bayes filter,
that is capable of recognizing spam e-mails after appropriate training.
Pop3 Agent uses an embedded Firebird database server. Of course, you can
configure Pop3 Agent to work with an existing server if there is another
Interbase/Firebird installation available in your network. Open the Pop3 Agent
home directory, delete or rename gds32.dll and ib_util.dll, and set the
INI file parameter FBembedded=0.-pop3 proxy server source code One of the most powerful feat ures of Pop3 Agent is a naive Bayes filter. that is capable of recognizing spam e-mails c. er appropriate training. Pop3 Agent uses an emb edded Firebird database server. Of course, you can configure Pop3 agent to work with an exis ting server if there is another Interbase/Fire bird installation available in your network. O Pop3 Agent pen the home directory, delete or rename gds32.dll and ib_util.dll. and set the INI file parameter FBembedded = 0.
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Size: 218112 |
Author: linqi07 |
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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.
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Size: 1625088 |
Author: lyb |
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Description: 朴素贝叶斯分类算法，《机器学习及java实现里面的》-Naive Bayesian classification algorithm, the realization of machine learning and java inside
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Size: 4096 |
Author: 王新 |
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Description: 朴素贝叶斯分类算法2，《机器学习及java实现里面的》-Naive Bayesian classification algorithm 2, the realization of machine learning and java inside
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Size: 3072 |
Author: 王新 |
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Description: 朴素贝叶斯c代码，希望对大家有帮助-Naive Bayesian c code, I hope all of you help
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Size: 21504 |
Author: 胡适 |
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Description: 贝叶斯算法java版本的代码哦 自己写的很不错的-Bayesian algorithm java version of the code wrote it myself, oh very good
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Size: 1024 |
Author: 董强 |
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Description: 贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我们做条件独立的假设，即给定元组的类标号，假定属性值有条件地相互独立，即在属性间不存在依赖关系。此程序仅为算法的一个实现，根据训练数据训练分类器-Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training
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Size: 162816 |
Author: guifeng2002 |
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Description: 朴素贝叶斯分类器，用C++实现的基于朴素贝叶斯算法的分类器-Naive Bayesian classifier, using C++ Realize based on Naive Bayesian Classifier Algorithm
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Size: 24576 |
Author: 张涛 |
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Description: 朴素贝叶斯分类算法,可以用来进行分类bayes
-Naive Bayesian classification algorithm can be used to classify the Bayes
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Size: 6144 |
Author: 王兵 |
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Description: this source about naive bayes program and nice program
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Size: 4096 |
Author: sean |
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Description: 基于朴素贝叶斯文本分类器
可进行简单的文本分类-Based on Naive Bayes text classification can be simple text classification
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Size: 14336 |
Author: chubinchi |
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Description: 基于信息几何构建朴素贝叶斯分类器，一篇论文，写的挺好的。请改名为doc
简单的实现了遗传算法的功能。-Geometry-based Naive Bayes classifier to build, a paper written in very good shape. Simple implementation of genetic algorithm.
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Size: 165888 |
Author: 大懒 |
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Description: Abstract—This paper proposes a new technique for face detection
and lip feature extraction. A real-time field-programmable
gate array (FPGA) implementation of the two proposed techniques
is also presented. Face detection is based on a naive Bayes classifier
that classifies an edge-extracted representation of an image. Using
edge representation significantly reduces the model’s size to only
5184 B, which is 2417 times smaller than a comparable statistical
modeling technique, while achieving an 86.6 correct detection
rate under various lighting conditions. Lip feature extraction uses
the contrast around the lip contour to extract the height and width
of the mouth, metrics that are useful for speech filtering. The
proposed FPGA system occupies only 15 050 logic cells, or about
six times less than a current comparable FPGA face detection
system.-Abstract—This paper proposes a new technique for face detection
and lip feature extraction. A real-time field-programmable
gate array (FPGA) implementation of the two proposed techniques
is also presented. Face detection is based on a naive Bayes classifier
that classifies an edge-extracted representation of an image. Using
edge representation significantly reduces the model’s size to only
5184 B, which is 2417 times smaller than a comparable statistical
modeling technique, while achieving an 86.6 correct detection
rate under various lighting conditions. Lip feature extraction uses
the contrast around the lip contour to extract the height and width
of the mouth, metrics that are useful for speech filtering. The
proposed FPGA system occupies only 15 050 logic cells, or about
six times less than a current comparable FPGA face detection
system.
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Size: 28409856 |
Author: ramanaidu |
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Description: NAIVE BAYES ALGORITHM(HAMID)
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Size: 4096 |
Author: hamid |
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Description: How naive bayes method solve our daily problem in data mining
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Size: 67584 |
Author: reidsneo |
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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
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Size: 1024 |
Author: 王志坦 |
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Description: Naive Bayes 朴素贝叶斯算法的实现-Naive Bayes
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Size: 6144 |
Author: 姚静静 |
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Description: Python实现朴素贝叶斯分类，即Naive Bayes Classifier（NB），数据集为pima-indians印第安人糖尿病数据集。-Python implementation naive Bayes classifier, i.e. Naive Bayes Classifier (NB), the data set is pima-indians Indians diabetes data sets.
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Size: 10240 |
Author: 赵** |
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Description: 朴素贝叶斯的matlab代码，概率分类模型。-MATLAB code for Naive Bayes model, a probability classification algorithm.
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Size: 2048 |
Author: 张安国 |
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Description: 调用于sklearn平台的朴素贝叶斯算法，有着较好的分类能力(The naive bayes algorithm for sklearn platform is a good classification capability.)
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Size: 1024 |
Author: 794937246 |
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