The effects of land uses on residential property values are crucial when evaluating costs and benefits of land projects for the purpose of public policy prescription or business decision making. It is widely recognized that a nuclear plant or a prison, for example, may often have an adverse effect on the property values of the nearby houses, while a park, a museum or a university usually has a ...

Belief revision is a well-research topic within AI. We argue that the new model of distributed belief revision as discussed here is suitable for general modelling of judicial decision making, along with extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment wi...

I present LIBPHON, a nonparametric regression-based model of phonological acquisition that induces a generalised and productive pattern of vowel harmony—including opaque and transparent neutrality—on the basis of simplified formant data. The model quickly learns to generate harmonically correct morphologically complex forms to which it has not been exposed. 1 Explaining phonological patterns Ho...

I present LIBPHON, a nonparametric regression-based model of phonological acquisition that induces a generalised and productive pattern of vowel harmony—including opaque and transparent neutrality—on the basis of simplified formant data. The model quickly learns to generate harmonically correct morphologically complex forms to which it has not been exposed. 1 Explaining phonological patterns Ho...

4 Abdollahi Effects of Subliminally-Primed Bodily Positions and States on JDM 4 Adelman Confirmation Bias in Complex Analyses 4 Balaji Rao et al Application of Brunswikian Theory to Corrosion Damage Assessment of Reinforced Concrete Structural Members 5 Brehaut How Decision Rules are Applied in Practice: A Clinical judgment analysis 6 Carvalho The Use of JA and SJT in Legislative Research 6 Dha...

Machine learning has provided powerful algorithms that automatically generate predictive models from experience. One specific technique is supervised learning, where the machine is trained to predict a desired output for each input pattern x. This chapter will focus on classification, that is, supervised learning when the output to predict is a class label. For instance predicting whether a pat...

The classification of dyslexic children into discrete subtypes yields a poor description of the dyslexic population at large. Multiple regression methods were used to examine continuous variation in component reading subskills (nonword and exception word reading) and their underlying cognitive skills within a group of 59 9-15 year-old dyslexic children. Two measures of phonological skills contr...

Generalized additive models are useful in finding predictor-response relationships in many kinds of data without using a specific model. They combine the ability to explore many nonparametric relationships simultaneously with the distributional flexibility of generalized linear models. The approach often brings to light nonlinear dependency structures in your data. This paper discusses an examp...

Diese Dissertation wurde selbständig, ohne unerlaubte Hilfen erarbeitet. Acknowledgements I would like to thank Prof. F. Ulrich Hartl for providing me the opportunity to work under his guidance and introducing me to the fascinating field of Chaperone mediated protein folding. His constant support, encouragement, ideas and often stimulating discussions were important for the fruitful completion ...

The notion of a boosting algorithm was originally introduced by Valiant in the context of the “probably approximately correct” (PAC) model of learnability [19]. In this context boosting is a method for provably improving the accuracy of any “weak” classification learning algorithm. The first boosting algorithm was invented by Schapire [16] and the second one by Freund [2]. These two algorithms ...