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...

We address in this paper the question of how the knowledge of the marginal distribution P (x) can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We also propose practical implementations.

In opinion mining, there has been only very little work investigating semi-supervised machine learning on document-level polarity classification. We show that semi-supervised learning performs significantly better than supervised learning when only few labeled data are available. Semi-supervised polarity classifiers rely on a predictive feature set. (Semi-)Manually built polarity lexicons are o...

We report research toward a never-ending language learning system, focusing on a first implementation which learns to classify occurrences of noun phrases according to lexical categories such as “city” and “university.” Our experiments suggest that the accuracy of classifiers produced by semi-supervised learning can be improved by coupling the learning of multiple classes based on background kn...

Abstract: Resistance testing is an important tool in today’s anti-HIV therapy management for improving the success of antiretroviral therapy. Routinely, the genetic sequence of viral target proteins is obtained. These sequences are then inspected for mutations that might confer resistance to antiretroviral drugs. However, interpretation of the genomic data is challenging. In recent years, appro...

This paper presents a semi-supervised machine-learning approach to predicting whether students will be successful in solving problem-solving tasks within narrative-centered learning environments. Results suggest the approach often outperforms standard supervised learning methods.

We show that for linear classifiers defined by convex marginbased surrogate losses that are monotonically decreasing, it is impossible to construct any semi-supervised approach that is able to guarantee an improvement over the supervised classifier measured by this surrogate loss. For non-monotonically decreasing loss functions, we demonstrate safe improvements are possible.

In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficien...

We relate function class complexity to structure in the function domain. This facilitates risk analysis relative to cluster structure in the input space which is particularly effective in semi-supervised learning. In particular we quantify the complexity of function classes defined over a graph in terms of the graph structure.

2 Clustering Techniques: A Brief Survey 4 2.1 Partitional Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Hierarchical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Discriminative vs. Generative Models . . . . . . . . . . . . . . . . . 12 2.4 Assessment of Results . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4.1 Internal (model-based, unsup...