5. A mobile phone comprising a memory, a processor configured to run computer-readable program code in the memory, and a file system, the file system comprising: a support vector machine (SVM) learning model comprising a threshold value, a feature set, and score values for features in the feature set, the SVM learning model being derived from an intermediate SVM learning model generated in a computer external to the mobile phone by training a support vector machine using a plurality of sample spam text messages and a plurality of sample legitimate text messages, the score values comprising weight values assigned to features in the feature set; a parser configured to parse a text message in the mobile phone for features noted in the SVM learning model; and an anti-spam engine configured to determine whether or not the text message is a spam text message based on weights of features noted in the SVM learning model and found in the text message without converting the text message to a vector in the mobile phone.
5. A mobile phone comprising a memory, a processor configured to run computer-readable program code in the memory, and a file system, the file system comprising: a support vector machine (SVM) learning model comprising a threshold value, a feature set, and score values for features in the feature set, the SVM learning model being derived from an intermediate SVM learning model generated in a computer external to the mobile phone by training a support vector machine using a plurality of sample spam text messages and a plurality of sample legitimate text messages, the score values comprising weight values assigned to features in the feature set; a parser configured to parse a text message in the mobile phone for features noted in the SVM learning model; and an anti-spam engine configured to determine whether or not the text message is a spam text message based on weights of features noted in the SVM learning model and found in the text message without converting the text message to a vector in the mobile phone. 6. The mobile phone of claim 5 wherein the anti-spam engine determines whether or not the text message is a spam text message by retrieving from the SVM learning model a weight of each feature found in the text message, adding the weights of all features found in the text message to generate a total score, and comparing the total score to the threshold.