Modeling the Example Life-Cycle in an Online Classification Learner

Abstract. An online classification system maintained by a learner can be subject to latency and filtering of training examples which can impact on its classification accuracy especially under concept drift. A life-cycle model is developed to provide a framework for studying this problem. Meta data emerges from this model which it is proposed can enhance online learning systems. In particular, the definition of the time-stamp of an example, as currently used in the literature, is shown to be problematic and an alternative is proposed

Bringing IPTV to the Market through Differentiated Service Provisioning

The world of telecommunications continues to provide radical technologies. Offering the benefits of a superior television experience at reduced long-term costs, IPTV is the newest offering. Deployments, however, are slow to be rolled out; the hardware and software support necessary is not uniformly available. This paper examines the challenges in providing IPTV services and the limitations in developments to overcome these challenges. Subsequently, a proposal is made which attempts to help solve the challenge of fulfilling real-time multimedia transmissions through provisioning for differentiated services. Initial implementations in Opnet are documented, and the paper concludes with an outline of future work

Real-time rule-based classification of player types in computer games

The power of using machine learning to improve or investigate the experience of play is only beginning to be realised. For instance, the experience of play is a psychological phenomenon, yet common psychological concepts such as the typology of temperaments have not been widely utilised in game design or research. An effective player typology provides a model by which we can analyse player behaviour. We present a real-time classifier of player type, implemented in the test-bed game Pac-Man. Decision Tree algorithms CART and C5.0 were trained on labels from the DGD player typology (Bateman and Boon, 21st century game design, vol. 1, 2005). The classifier is then built by selecting rules from the Decision Trees using a rule- performance metric, and experimentally validated. We achieve 70% accuracy in this validation testing. We further analyse the concept descriptions learned by the Decision Trees. The algorithm output is examined with respect to a set of hypotheses on player behaviour. A set of open questions is then posed against the test data obtained from validation testing, to illustrate the further insights possible from extended analysis.Peer reviewe