667 research outputs found
Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm
The Markov Blanket Bayesian Classifier is a recently-proposed algorithm for
construction of probabilistic classifiers. This paper presents an empirical
comparison of the MBBC algorithm with three other Bayesian classifiers: Naive
Bayes, Tree-Augmented Naive Bayes and a general Bayesian network. All of these
are implemented using the K2 framework of Cooper and Herskovits. The
classifiers are compared in terms of their performance (using simple accuracy
measures and ROC curves) and speed, on a range of standard benchmark data sets.
It is concluded that MBBC is competitive in terms of speed and accuracy with
the other algorithms considered.Comment: 9 pages: Technical Report No. NUIG-IT-011002, Department of
Information Technology, National University of Ireland, Galway (2002
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Residential broadband subscription demand: an econometric analysis of Australian choice experiment data
The recent roll-out of fibre-optic cable suggests that the willingness of households in passed communities to subscribe to networked services is an important issue. This paper studies the determination of the demand for network subscription. Through a discrete choice model the effect of installation and rental price on the likelihood of subscription is analysed. The logit regression is based on choice experiment (stated preference)subscription data obtained from a national survey of households. Limitations of this preliminary work and suggestions for future research are discussed.Broadband subscription demand
Broadband delivered entertainment services: forecasting Australian subscription intentions
This study estimates a nested multinomial logit (NMNL) model of broadband delivered entertainment service subscription that allows for the impact of an installation fee and rental price, service attributes and household demographic variables on subscription. The model is estimated on stated-preference data obtained from an Australia-wide survey of capital cities and provincial centres. Nested multinomial logit model estimates are used to provide forecasts that suggest 65 per cent of separate residences passed are likely to subscribe at 2000. This percentage translates into 1237 744 subscriber.Broadband entertainment services; forecasting Australian subscription demand
Mobile telephony and internet growth: impacts on consumer welfare
Innovation in digital technology has allowed rapid growth in mobile telephone and Internet adoption among consumers. The implication underlying the high rates of subscription growth is that consumers generally place a high valuation on telecommunication services. Moreover, since mobile telephone and Internet are predominantly telecommunication services, it is reasonable to presume that the network effect may be largely responsible for this growth. The implication of the network effect, where the consumerâs valuation of service increases with the size of the network is that subscription growth is endogenous. However, to date there have been few attempts to measure the change in consumer welfare as networks increase. Following Hausman (1981), this paper measures the change in consumer surplus based on the compensating variations approach. The result is an annual measure of the change in consumer surplus for the representative consumer for the OECD region. In addition, the approach reveals whether marginal consumer surplus is a decreasing or increasing function of network size. Measurement of the change in consumer welfare thus provides an additional tool for public policy analysis.Consumer welfare; network effect; compensating variation
Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning
In this paper, we introduce a skill-balancing mechanism for adversarial
non-player characters (NPCs), called Skilled Experience Catalogue (SEC). The
objective of this mechanism is to approximately match the skill level of an NPC
to an opponent in real-time. We test the technique in the context of a
First-Person Shooter (FPS) game. Specifically, the technique adjusts a
reinforcement learning NPC's proficiency with a weapon based on its current
performance against an opponent. Firstly, a catalogue of experience, in the
form of stored learning policies, is built up by playing a series of training
games. Once the NPC has been sufficiently trained, the catalogue acts as a
timeline of experience with incremental knowledge milestones in the form of
stored learning policies. If the NPC is performing poorly, it can jump to a
later stage in the learning timeline to be equipped with more informed
decision-making. Likewise, if it is performing significantly better than the
opponent, it will jump to an earlier stage. The NPC continues to learn in
real-time using reinforcement learning but its policy is adjusted, as required,
by loading the most suitable milestones for the current circumstances.Comment: IEEE Conference on Computational Intelligence and Games (CIG). August
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