Lightweight SVM-based content filtering system for mobile phones
First Claim
1. A method of classifying text messages in a mobile phone, the method comprising:
- training a support vector machine using a plurality of sample spam text messages and a plurality of sample legitimate text messages in a server computer separate from the mobile phone during a training stage to generate an intermediate support vector machine learning model that includes a threshold value and support vectors;
deriving the support vector machine (SVM) learning model from the intermediate support vector machine learning model by storing in the SVM learning model the threshold value but not the support vectors from the intermediate support vector machine learning model, a feature set, and score values comprising weights assigned to features in the feature set;
providing the SVM learning model in the mobile phone,extracting features from a text message in the mobile phone to generated extracted features;
retrieving from the SVM learning model a corresponding score value for each of the extracted features;
adding score values of the extracted features to generate a total score; and
comparing the total score to the threshold value to determine whether or not the text message is a spam text message.
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Abstract
In one embodiment, a content filtering system generates a support vector machine (SVM) learning model in a server computer and provides the SVM learning model to a mobile phone for use in classifying text messages. The SVM learning model may be generated in the server computer by training a support vector machine with sample text messages that include spam and legitimate text messages. A resulting intermediate SVM learning model from the support vector machine may include a threshold value, support vectors and alpha values. The SVM learning model in the mobile phone may include the threshold value, the features, and the weights of the features. An incoming text message may be parsed for the features. The weights of features found in the incoming text message may be added and compared to the threshold value to determine whether or not the incoming text message is spam.
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Citations
14 Claims
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1. A method of classifying text messages in a mobile phone, the method comprising:
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training a support vector machine using a plurality of sample spam text messages and a plurality of sample legitimate text messages in a server computer separate from the mobile phone during a training stage to generate an intermediate support vector machine learning model that includes a threshold value and support vectors; deriving the support vector machine (SVM) learning model from the intermediate support vector machine learning model by storing in the SVM learning model the threshold value but not the support vectors from the intermediate support vector machine learning model, a feature set, and score values comprising weights assigned to features in the feature set; providing the SVM learning model in the mobile phone, extracting features from a text message in the mobile phone to generated extracted features; retrieving from the SVM learning model a corresponding score value for each of the extracted features; adding score values of the extracted features to generate a total score; and comparing the total score to the threshold value to determine whether or not the text message is a spam text message. - View Dependent Claims (2, 3, 4)
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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:
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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. - View Dependent Claims (6, 7, 8)
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9. A method of classifying text messages wirelessly received in a mobile phone, the method comprising:
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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. - View Dependent Claims (10, 11, 12, 13, 14)
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Specification