Goal-based structuring in a recommender systems

Recommender systems help people to find information that is interesting to them. However, current recommendation techniques only address the user's short-term and long-term interests, not their immediate interests. This paper describes a method to structure information (with or without using recommendations) taking into account the users' immediate interests: a goal-based structuring method. Goal-based structuring is based on the fact that people experience certain gratifications from using information, which should match with their goals. An experiment using an electronic TV guide shows that structuring information using a goal-based structure makes it easier for users to find interesting information, especially if the goals are used explicitly; this is independent of whether recommendations are used or not. It also shows that goal-based structuring has more influence on how easy it is for users to find interesting information than recommendations

Galaxy bulges and their massive black holes: a review

With references to both key and oft-forgotten pioneering works, this article starts by presenting a review into how we came to believe in the existence of massive black holes at the centres of galaxies. It then presents the historical development of the near-linear (black hole)-(host spheroid) mass relation, before explaining why this has recently been dramatically revised. Past disagreement over the slope of the (black hole)-(velocity dispersion) relation is also explained, and the discovery of sub-structure within the (black hole)-(velocity dispersion) diagram is discussed. As the search for the fundamental connection between massive black holes and their host galaxies continues, the competing array of additional black hole mass scaling relations for samples of predominantly inactive galaxies are presented.Comment: Invited (15 Feb. 2014) review article (submitted 16 Nov. 2014). 590 references, 9 figures, 25 pages in emulateApJ format. To appear in "Galactic Bulges", E. Laurikainen, R.F. Peletier, and D.A. Gadotti (eds.), Springer Publishin

A connectionist approach for supporting personalized learning in a web-based learning environment

The paper investigates the use of computational intelligence for adaptive lesson presentation in a Web-based learning environment. A specialized connectionist architecture is developed and a formulation of the planning strategy retrieval in the context of the network dynamics is proposed to select the content of the lesson in a goal-oriented way of ’teaching’. The educational material of the course is stored in a connectionist-based distributed information storage system that provides capabilities for optimal selection of the educational material according to the knowledge needs, abilities and preferences of each learner. Low-level tests of the system have been performed to investigate how the connectionist architecture and the learner model function together to create an operational learning environment. Preliminary experiments indicate that personalized content delivery is provided in an educational effective way. © Springer-Verlag Berlin Heidelberg 200

Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods

This article focuses on gradient-based backpropagation algorithms that use either a common adaptive learning rate for all weights or an individual adaptive learning rate for each weight and apply the Goldstein/Armijo line search. The learning-rate adaptation is based on descent techniques and estimates of the local Lipschitz constant that are obtained without additional error function and gradient evaluations. The proposed algorithms improve the backpropagation training in terms of both convergence rate and convergence characteristics, such as stable learning and robustness to oscillations. Simulations are conducted to compare and evaluate the convergence behavior of these gradient-based training algorithms with several popular training methods

Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information

Intelligent computerised systems can provide useful assistance to the physician in the rapid identification of tissue abnormalities and accurate diagnosis in real-time. This paper reviews basic issues in medical imaging and neural network-based systems for medical image interpretation. In the framework of intelligent systems, a simple scheme that has been implemented is presented as an example of the use of intelligent systems to discriminate between normal and cancerous regions in colonoscopic images. Preliminary results indicate that this scheme is capable of high accuracy detection of abnormalities within the image. It can also be successfully applied to different types of images, to detect abnormalities that belong to different cancer types

Using simulated students for machine learning

In this paper we present how simulated students have been generated in order to obtain a large amount of labeled data for training and testing a neural network-based fuzzy model of the student in an Intelligent Learning Environment (ILE). The simulated students have been generated by modifying real students' records and classified by a group of expert teachers regarding their learning style category. Experimental results were encouraging, similar to experts' classifications. © Springer-Verlag 2004

A Class of Gradient Unconstrained Minimization Algorithms With Adaptive Stepsize

In this paper the development, convergence theory and numerical testing of a class of gradient unconstrained minimization algorithms with adaptive stepsize are presented. The proposed class comprises four algorithms: the first two incorporate techniques for the adaptation of a common stepsize for all coordinate directions and the other two allow an individual adaptive stepsize along each coordinate direction. All the algorithms are computationally efficient and possess interesting convergence properties utilizing estimates of the Lipschitz constant that are obtained without additional function or gradient evaluations. The algorithms have been implemented and tested on some well-known test cases as well as on real-life artificial neural network applications and the results have been very satisfactory