Titlebook: Research and Development in Intelligent Systems XXV; Proceedings of AI-20 Max Bramer,Miltos Petridis,Frans Coenen Conference proceedings 20

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Sparse Representations for Pattern Classification using Learned Dictionariesng sparse and overcomplete representations have been developed. Data-dependent representations using learned dictionaries have been significant in applications such as feature extraction and denoising. In this paper, our goal is to perform pattern classification in a domain referred to as the data r

陈腐的人

Qualitative Hidden Markov Models for Classifying Gene Expression Datain areas such as speech recognition and bioinformatics. While variations of traditional HMMs proved to be practical in applications where it is feasible to obtain the numerical probabilities required for the specification of the parameters of the model and the probabilities available are descriptive

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Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs times. In this paper, we show how Evolutionary algorithms (EA) can be used to analyse the effect of demand uncertainty in dynamic pricing. The experiments are conducted in a range of dynamic pricing problems considering a number of different stochastic scenarios with a number of different EAs. The

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elucidate

Constraint Satisfaction and Fixes: Revisiting Sisyphus VTtrAKTor work presented at EKAW 2006. ExtrAKTor takes a Protégé KB describing a propose and- revise (PnR) problem, including both constraints & fixes. Subsequently, it extracts and transforms these components so that they are directly usable by the ECLiPSe CSP toolkit to solve a range of configuratio

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On a Control Parameter Free Optimization Algorithmtegy with a stopping criterion and a heuristic for selecting the population size for the hill-climbing component. Experiments presented in this paper demonstrate that the algorithm is very effective and also very efficient whilst removing the need for tuning the algorithm to match an optimization pr