Perception and experience gained in the contemporary school could not help human beings' active learning. Totally, participation is the main element in active learning and thus, the active participation of students in the learning process is emphasized by education and learning in secondary schools. Given the importance of active learning, in this paper, the effective components in this type of...

We propose novel semi-supervised and active learning algorithms for the problem of community detection on networks. The algorithms are based on optimizing the likelihood function of the community assignments given a graph and an estimate of the statistical model that generated it. The optimization framework is inspired by prior work on the unsupervised community detection problem in Stochastic ...

This report documents the program and the outcomes of Dagstuhl Seminar 16382 “Foundations of Unsupervised Learning”. Unsupervised learning techniques are frequently used in practice of data analysis. However, there is currently little formal guidance as to how, when and to what effect to use which unsupervised learning method. The goal of the seminar was to initiate a broader and more systemati...

This research aimed to discriminate between 2 general approaches to unsupervised category learning, one based on learning explicit correlational rules or associations within a stimulus domain (autocorrelation) and the other based on inventing separate categories to capture the correlational structure of the domain (category invention). An "attribute-listing" paradigm was used to index unsupervi...

In this article we extend the (recently published) unsupervised information theoretic vector quantization approach based on the Cauchy–Schwarz-divergence for matching data and prototype densities to supervised learning and classification. In particular, first we generalize the unsupervised method to more general metrics instead of the Euclidean, as it was used in the original algorithm. Thereaf...

Unsupervised learning of relative visual attributes is important because it is often infeasible for a human annotator to predefine and manually label all the relative attributes in large datasets. We propose a method for learning relative visual attributes given a set of images for each training class. The method is unsupervised in the sense that it does not require a set of predefined attribut...

This paper proposes an unsupervised algorithm for learning a finite Dirichlet mixture model. An important part of the unsupervised learning problem is determining the number of clusters which best describe the data. We consider here the application of the Minimum Message length (MML) principle to determine the number of clusters. The Model is compared with results obtained by other selection cr...