naive bayes text classification tutorial

(tk|c) = (the sum of the number of occurrences of the word tk in each document in Class C)/(the total number of words under Class C +| v|). V is the word list of the training sample (that is, the word is extracted, the word appears multiple times, and only one is counted), | V| is the number of words that the training sample contains. P (tk|c) can be seen as the evidence that word TK provides in proving that D belongs to Class C, while P (c) can be considered as a percentage of the ov

). When sorting, an example of X is given, and all of the P (y|x) is found in a pile of posteriori probabilities, the largest of which is the category x belongs to. According to the Bayesian formula, the posterior probability is P (y| X) =p (x| y) P (Y) p (X) When comparing the posteriori probabilities of different Y-values, the denominator p (X) is always constant, so it can be ignored . The priori probability P (Y) can be easily estimated by calculating the proportion of training samples that

PHP text database classification and sorting implementation. In PHP programming, if you use a text-based database, it may be a headache for its classification sorting problem. The following section describes how to use PHP and JavaScript scripts to implement this function. in PHP programming, if you use a

arranged subscript $key_array[], and then reading the file in sequence by the subscript array. Below we write this PHP file. 　　 OK, our program has been written. The program read the file two times, this may be slower, of course, you can change the first time to read the contents of the file stored in an array, so as to avoid the second reading, but this requires more server memory, for security had to sacrifice a little speed. Today Xiao Yang not only introduced the