Adaptive learning

Abstract: Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.

Abstract: Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.

Abstract: Suggests a reorientation of game theory in which players are not hyper-rational and knowledge is incomplete; postulates a simple adaptive learning process; and applies this framework to the study of social and economic institutions. Discusses learning; dynamic and stochastic stability; adaptive learning in small games; variations on the learning process; local interaction; equilibrium and disequilibrium selection in general games; bargaining; and contracts. Young is Scott and Barbara Black Professor of Economics at Johns Hopkins University. Bibliography; index.

Abstract: Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

Abstract: This review of literature and research into the effectiveness of distance education systems deals with a number of factors which affect their success or failure. These include the influence of distance learning theory upon instructional design and delivery, redefining the roles of partners in distance education teams, media selection, technology adoption, change implementation, methods and strategies to increase interactivity, inquiry, and active learning, learner characteristics and modes of learning, teacher mediation and learner support, operational issues, policy and management issues, and cost/ benefit tradeoffs. It is intended as a companion piece to Sherry and Morse’s (1994) training needs assessment.