9. A method of classifying text messages wirelessly received in a mobile phone, the method comprising: in a server computer, training a support vector machine using a plurality of sample text messages comprising sample spam text messages and sample legitimate text messages to generate a first support vector machine (SVM) learning model, the first SVM learning model comprising a threshold value, a feature set, and score values for features in the feature set; providing the first SVM learning model to a mobile phone; and using the first SVM learning model in the mobile phone to classify a text message in the mobile phone without converting the text message to a vector in the mobile phone.
9. A method of classifying text messages wirelessly received in a mobile phone, the method comprising: in a server computer, training a support vector machine using a plurality of sample text messages comprising sample spam text messages and sample legitimate text messages to generate a first support vector machine (SVM) learning model, the first SVM learning model comprising a threshold value, a feature set, and score values for features in the feature set; providing the first SVM learning model to a mobile phone; and using the first SVM learning model in the mobile phone to classify a text message in the mobile phone without converting the text message to a vector in the mobile phone. 12. The method of claim 9 wherein using the first SVM learning model in the mobile phone to classify the text message comprises: parsing the text message to extract features from the text message, the extracted features being identified in the first SVM learning model; consulting the first SVM learning model for score values of the extracted features; and comparing the score values to the threshold included in the first SVM learning model.