Electrical computers and digital processing systems: multicomput – Computer conferencing – Demand based messaging
Reexamination Certificate
2011-04-19
2011-04-19
Shin, Kyung-Hye (Department: 2443)
Electrical computers and digital processing systems: multicomput
Computer conferencing
Demand based messaging
C709S225000, C726S022000, C707S793000
Reexamination Certificate
active
07930353
ABSTRACT:
Decision trees populated with classifier models are leveraged to provide enhanced spam detection utilizing separate email classifiers for each feature of an email. This provides a higher probability of spam detection through tailoring of each classifier model to facilitate in more accurately determining spam on a feature-by-feature basis. Classifiers can be constructed based on linear models such as, for example, logistic-regression models and/or support vector machines (SVM) and the like. The classifiers can also be constructed based on decision trees. “Compound features” based on internal and/or external nodes of a decision tree can be utilized to provide linear classifier models as well. Smoothing of the spam detection results can be achieved by utilizing classifier models from other nodes within the decision tree if training data is sparse. This forms a base model for branches of a decision tree that may not have received substantial training data.
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Chickering David M.
Goodman Joshua T.
Heckerman David E.
Hulten Geoffrey J.
Meek Christopher A.
Lee & Hayes PLLC
Microsoft Corporation
Shin Kyung-Hye
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