is usually easy to obtain. Semi-supervised learning addresses the problem of utilizing unlabeled data along with supervised labeled data, to build better classifiers. In this paper we introduce a semi-supervised approach based on mutual reinforcement in graphs to obtain more labeled data to enhance

labeled and unlabeled data to learn better models of user behavior that can be used to predict search success more effectively. We present a semi-supervised approach to modeling Web search satisfaction. The proposed approach can use either labeled data only or both labeled and unlabeled data. We show

In this paper, we present a semi-supervised approach to space carving by casting the recovery of volumetric data from multiple views into an evidence combining setting. The method presented here is statistical in nature and employs, as a starting point, a manually obtained contour. By making use

Abstract. This paper presents a machine learning approach to question classification. We have defined a kernel function based on latent semantic information acquired from unlabeled data. This kernel allows including external semantic knowledge into the supervised learning process. We have combined

on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled data, and show that our methodology outperforms existing supervised and unsupervised approaches. We also identify salient features useful for music genre classification. We achieve 97.1 % accuracy

This paper proposes a novel semisupervised word alignment technique called EMDC that integrates discriminative and generative methods. A discriminative aligner is used to find high precision partial alignments that serve as constraints for a generative aligner which implements a constrained version

behaviors against its descriptions. Data about known malwares are not used in their work, which limits its effectiveness. In this work, we extend the work by Gorla et al. by proposing an active and semi-supervised approach for detecting malwares. Different from CHABADA, our approach will make use of both

Abstract. Semi-supervised Learning is a machine learning approach that, by making use of both labeled and unlabeled data for training, can significantly improve learning accuracy. Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy

Permanent City Research Online

Information extraction (IE) from semi-structured Web documents plays an important role for a variety of information agents. Over the past few years, researchers have developed a rich family of generic IE techniques based on supervised approaches which learn extraction rules from user