We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire population, not just conditioned on the logging policy. Prior work addresses this problem either when only logged data is available, or purely in a controlled random experimentation setting where the logged data is igno...

The current emphasis in the promotion of school learning is on the active involvement of the learners in the learning process. Helping students to develop interest in self-directed cooperative learning is considered to be one of the ways to enhance active learning. This study sought to investigate the views of selected undergraduate education students on the value and constraints of cooperative...

One of the goals of machine learning researches is to build accurate classifiers form an amount of labeled examples. In some problems, it is necessary to gather a large set of labeled examples which can be costly and time-consuming. To reduce these expenses, one can use active learning algorithms. These algorithms benefit from the possibility of performing a small number of label-queries from a...

In many potential applications of machine learning, unlabelled data are abundantly available at low cost, but there is a paucity of labelled data, and labeling unlabelled examples is expensive and/or time-consuming. This motivates the development of active learning methods, that seek to direct the collection of labelled examples such that the greatest performance gains can be achieved using the...

Purpose – The purpose of this study was to introduce a sustainability course to interior design students and explore how working with industry could address challenges with integrating sustainability education into and ensuring student motivation in non-studio courses. Design/methodology/approach – This is a case study presenting qualitative evaluation from the 15-week “IAED 342 Sustainable Des...

How can a doctor diagnose new diseases with little historical knowledge, which are emerging over time? Active learning is a promising way to address the problem by querying the most informative samples. Since the diagnosed cases for new disease are very limited, gleaning knowledge from other domains (classical prescriptions) to prevent the bias of active leaning would be vital for accurate diag...

Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of v...

The problem of designing input signals for optimal generalization in supervised learning is called active learning. In many active learning methods devised so far, the bias of the learning results is assumed to be zero. In this paper, we remove this assumption and propose a new active learning method with the bias reduction. The effectiveness of the proposed method is demonstrated through compu...

Software-mediated learning requires adjustments in the teaching and learning process. In particular active learning facilitated through interactive learning software differs from traditional instructor-oriented, classroom-based teaching. We present behaviour analysis techniques for Web-mediated learning. Motivation, acceptance of the learning approach and technology, learning organisation and a...

This paper uses an ensemble of classifiers and active learning strategies to predict radiologists’ assessment of the nodules of the Lung Image Database Consortium (LIDC). In particular, the paper presents machine learning classifiers that model agreement among ratings in seven semantic characteristics: spiculation, lobulation, texture, sphericity, margin, subtlety, and malignancy. The ensemble ...