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.

This paper analyzes the performance of semisupervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We present a mathematical analysis of this “degradation” phenomenon and show that it is due to the fact that bias may be adversely affected by unlabeled ...

The focus of this thesis is on learning approaches for what we call “low-quality data” and in particular data in which only small amounts of labeled target data is available. The first part provides background discussion on low-quality data issues, followed by preliminary study in this area. The remainder of the thesis focuses on a particular scenario: multi-view semi-supervised learning. Multi...

Partial cognates are pairs of words in two languages that have the same meaning in some, but not all contexts. Detecting the actual meaning of a partial cognate in context can be useful for Machine Translation tools and for Computer-Assisted Language Learning tools. In this paper we propose a supervised and a semisupervised method to disambiguate partial cognates between two languages: French a...

Temporality is crucial in understanding the course of clinical events from a patient’s electronic health records and temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and...

Intelligent systems in automobiles need to be aware of the driving and driver context. Available sensor data stream has to be modeled and monitored in order to do so. Currently there exist no building blocks for hierarchical modeling of driving. By semi-supervised segmentation such building blocks can be discovered. We call them drivemes in analogy to phonemes. More parsimonious modeling of dri...

We consider the problem of semi-supervised few-shot classification (when the few labeled samples are accompanied with unlabeled data) and show how to adapt the Prototypical Networks [10] to this problem. We first show that using larger and better regularized prototypical networks can improve the classification accuracy. We then show further improvements by making use of unlabeled data.

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...

Human chess players exhibit a large variation in the amount of time they allocate for each move. Yet, the problem of devising resource allocation strategies for game playing did not receive enough attention. In this paper we present a framework for studying resource allocation strategies. We de ne allocation strategy and identify three major types of strategies: static, semi-dynamic, and dynami...