In standard classification a training set of supervised instances is given. In a more general setup, some supervised instances are available, while further ones should be chosen from an unsupervised set and then annotated. As the annotation step is costly, active learning algorithms are used to select which instances to annotate to maximally increase the classification performance while annotat...

breast lesion segmentation in mr images is one of the most important parts of clinical diagnostic tools. pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. on t...

Active Learning Method (ALM) is a soft computing method which is used for modeling and control, based on fuzzy logic. Although ALM has shown that it acts well in dynamic environments, its operators cannot support it very well in complex situations due to losing data. Thus ALM can find better membership functions if more appropriate operators be chosen for it. This paper substituted two new oper...

Semi-supervised approaches have proven to be effective in clustering tasks. They allow user input, thus improving the quality of the clustering obtained, while maintaining a controllable level of user intervention. Despite being an important class of algorithms, hierarchical clustering has been little explored in semisupervised solutions. In this report, we address the problem of semi-supervise...

Unsupervised cluster analysis is proposed for analysis of active avoidance formation in three groups of albino rats: 1) Intact; 2) With electrolytic lesions of neocortex over the dorsal hippocampus; and 3) with electrolytic lesions of dorsal hippocampus. The term “behavior vector” has been introduced to assess quantitatively the behavior of rats while learning. The proposed approach enables to ...

transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. feature selection for transfer learning (f-mmd) is a simple and effective transfer learning method, which tackles the domain shift problem. f-mmd has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive perf...

The growth of internet and the evolution of supporting infrastructures motivated universities and other educational institutions to adopt new teaching methods. These unsupervised methods are focused on the dissemination of the educational material and the evaluation of the users through tests and activities. The user behavior is not monitored and the performance of users cannot be reasoned in m...

Over the past few years the question of whether the lecture is an effective teaching method has been one of the most heatedly debated topics in the field of higher education. While research on the effectiveness of lectures has been carried out since at least the 1960s, the value of the lecture has been increasingly questioned recently for a number of reasons that include waning lecture attendan...

Active learning is an essential tool to reduce manual annotation costs in the presence of large amounts of unsupervised data. In this paper, we introduce new active learning methods based on measuring the impact of a new example on the current model. This is done by deriving model changes of Gaussian process models in closed form. Furthermore, we study typical pitfalls in active learning and sh...

1. Introduction From a traditional point of view, knowledge exploration can be categorized into supervised learning and unsupervised learning (Jordan and Jacobs 1994). In the last decade, there have been research activities on supervised learning approaches and techniques, whereby class information is available before any knowledge exploration takes place. The most utilized approach is to achie...