垃圾邮件分类的预处理和标准化

主要包括：

• Lower-casing: The entire email is converted into lower case, so that captialization is ignored (e.g., IndIcaTE is treated the same as Indicate).
• Stripping HTML: All HTML tags are removed from the emails.Many emails often come with HTML formatting; we remove all the HTML tags, so that only the content remains.
• Normalizing URLs: All URLs are replaced with the text httpaddr".
• Normalizing Email Addresses: All email addresses are replaced with the text emailaddr".
• Normalizing Numbers: All numbers are replaced with the text umber".
• Normalizing Dollars: All dollar signs ($) are replaced with the text dollar".
• Word Stemming: Words are reduced to their stemmed form. For example, discount", discounts", discounted" and discounting" are all replaced with discount". Sometimes, the Stemmer actually strips additional characters from the end, so include", includes", included",and including" are all replaced with includ".
• Removal of non-words: Non-words and punctuation have been removed. All white spaces (tabs, newlines, spaces) have all been trimmed to a single space character.

对于的matlab代码，这简洁的！

% Lower case
email_contents = lower(email_contents);

% Strip all HTML
% Looks for any expression that starts with < and ends with > and replace
% and does not have any < or > in the tag it with a space
email_contents = regexprep(email_contents, '<[^<>]+>', ' ');

% Handle Numbers
% Look for one or more characters between 0-9
email_contents = regexprep(email_contents, '[0-9]+', 'number');

% Handle URLS
% Look for strings starting with http:// or https://
email_contents = regexprep(email_contents, ...
                           '(http|https)://[^s]*', 'httpaddr');

% Handle Email Addresses
% Look for strings with @ in the middle
email_contents = regexprep(email_contents, '[^s]+@[^s]+', 'emailaddr');

% Handle $ sign
email_contents = regexprep(email_contents, '[$]+', 'dollar');

来源：machine learning-Andrew Ng, https://www.coursera.org/learn/machine-learning/programming/e4hZk/support-vector-machines