We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm ...

We address the problem of learning to rank documents in a multilingual context, when reference ranking information is only partially available. We propose a multiview learning approach to this semisupervised ranking task, where the translation of a document in a given language is considered as a view of the document. Although both multiview and semi-supervised learning of classifiers have been ...

We propose a semi-supervised framework to handle diverse data formats or data with mixedtype attributes. Our preliminary results in clustering data with mixed numerical and categorical attributes show that the proposed semi-supervised framework gives better clustering results in the categorical domain. Thus the seeds obtained from clustering the numerical domain give an additional knowledge to ...

One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods are proposed, at the current stage, we still don’t have a complete understanding of their effective...

AbstrAct The insufficiency of labeled training samples is a major obstacle in automatic semantic analysis of large

In many real-world applications there are usually abundant unlabeled data but the amount of labeled training examples are often limited, since labeling the data requires extensive human effort and expertise. Thus, exploiting unlabeled data to help improve the learning performance has attracted significant attention. Major techniques for this purpose include semi-supervised learning and active l...

We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lowerbounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we p...

There has been a surge of interest in learning using a mix of labeled and unlabeled data. General approaches include semi-supervised learning and tranductive inference. In this paper we look at some of the unique ways in which unlabeled data can improve performance when doing link-based classification, the classification of objects making use of both object descriptions and the links between ob...

In this paper we investigate the effect of unlabeled data on generative classifiers in semi-supervised learning. We first characterize situations where unlabeled data cannot change estimates obtained with labeled data, and argue that such situations are unusual in practice. We then report on a large set of experiments involving labeled and unlabeled data, and demonstrate that unlabeled data can...