The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
In this paper, we present a semi-supervised learning method for web page classification, leveraging click logs to augment training data by propagating class labels to unlabeled si...
Soo-Min Kim, Patrick Pantel, Lei Duan, Scott Gaffn...
We propose a new transductive learning algorithm for learning optimal linear representations that utilizes unlabeled data. We pose the problem of learning linear representations a...
Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applie...
This paper explores in detail the use of Error Correcting Output Coding (ECOC) for learning text classifiers. We show that the accuracy of a Naive Bayes Classifier over text class...