1. A computerized method for semi-supervised learning for web page classification, comprising: receiving a set of web pages as training elements; labeling some of the elements of the set of training elements that are determined to fall within a classification group, the set of training elements thereby having labeled elements and unlabeled elements; using selected labeled elements and unlabeled elements as examples in a semi-supervised support vector machine implemented using a mean field annealing method, constructing a continuous loss function from a non-continuous loss function, train a linear classifier; receiving unclassified web pages; and classifying the unclassified web pages using the trained linear classifier.
1. A computerized method for semi-supervised learning for web page classification, comprising: receiving a set of web pages as training elements; labeling some of the elements of the set of training elements that are determined to fall within a classification group, the set of training elements thereby having labeled elements and unlabeled elements; using selected labeled elements and unlabeled elements as examples in a semi-supervised support vector machine implemented using a mean field annealing method, constructing a continuous loss function from a non-continuous loss function, train a linear classifier; receiving unclassified web pages; and classifying the unclassified web pages using the trained linear classifier. 3. The method of claim 1 , wherein the semi-supervised support vector machine uses a finite Newton method for fast training.