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[DataMining] Naive-bayes DL : 0: 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
Date : 2025-12-29 Size : 1kb User : 王志坦
[DataMining] LDA_ FDA_with_tutorial DL : 0: LDA降维是常用的降维手段之一，是常用的有监督学习降维工具。这个文件对其产生W后的使用进行了简要说明，使用W进行最终的降维可以得到十分漂亮的分析结果（在数据分布符合假设分析的情况下。）(LDA dimension reduction is one of the commonly used dimensionality reduction methods. It is a commonly used supervised learning dimensionality reduction tool. This file gives a brief description of the use of W after it was generated, and the final dimensionality reduction using W yields a very nice analysis result (if the data distribution conforms to the hypothetical analysis).)
Date : 2025-12-29 Size : 660kb User : SaoYear