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Yayın ICamp - The educational web for higher education(Springer Verlag, 2006) Kieslinger, Barbara; Wild, Fridolin; Arsun, Onur İhsaniCamp is an EC-funded research project in the area of Technology Enhanced Learning (TEL) that aims to support collaboration and social networking across systems, countries and disciplines in higher education. The concept of an iCamp Space will build on existing interfaces and integrate shared community features. Interoperability amongst different open source learning systems and tools is the key to successful sustainability of iCamp. The content for this collaboration within social communities is provided via distributed networked repositories including, for example, content brokerage platforms, online libraries, and learning object databases. The innovative pedagogical model of iCamp is based on social constructivist learning theories. iCamp creates an environment for a new way of social networking in higher education that puts more emphasis on self-organised, self-directed learning, social networking and cross-cultural collaboration.Yayın Integrating vendors into cooperative design practices(Taylor & Francis Ltd, 2009) Eskil, Mustafa Taner; Sticklen, JonThis paper describes a new approach to cooperative design using distributed, off-the-shelf design components. The ultimate goal is to enable assemblers to rapidly design their products and perform simulations using parts that are offered by a global network of suppliers. The obvious way to realise this goal would be to transfer desired component models to the client computer. However, in order to protect proprietary data, manufacturers are reluctant to share their design models without non-disclosure agreements, which can take in the order of months to put in place. Due to bandwidth limitations, it is also impractical to keep the models at the manufacturer site and do simulations by simple message passing. To deal with these impediments in e-commerce the modular distributed modelling (MDM) methodology is leveraged, which enables transfer of component models while hiding proprietary implementation details. MDM methodology with routine design (RD) methods are augmented to realise a platform (RD-MDM) that enables automatic selection of secured off-the-shelf design components over the Internet, integration of these components in an assembly, running simulations for design testing and publishing the approved product model as a secured MDM agent. This paper demonstrates the capabilities of the RD-MDM platform on a fuel cell-battery hybrid vehicle design example.Yayın Eigenclassifiers for combining correlated classifiers(Elsevier Science Inc, 2012-03-15) Ulaş, Aydın; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim EthemIn practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ulas, M. Semerci, O.T. Yildiz, E. Alpaydin, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298-1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets. Simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms. Second, we propose postprocessing before fusing using principal component analysis (PCA) to form uncorrelated eigenclassifiers from a set of correlated experts. Combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination, because using all allows redundancy.Yayın Model selection in omnivariate decision trees using Structural Risk Minimization(Elsevier Science Inc, 2011-12-01) Yıldız, Olcay TanerAs opposed to trees that use a single type of decision node, an omnivariate decision tree contains nodes of different types. We propose to use Structural Risk Minimization (SRM) to choose between node types in omnivariate decision tree construction to match the complexity of a node to the complexity of the data reaching that node. In order to apply SRM for model selection, one needs the VC-dimension of the candidate models. In this paper, we first derive the VC-dimension of the univariate model, and estimate the VC-dimension of all three models (univariate, linear multivariate or quadratic multivariate) experimentally. Second, we compare SRM with other model selection techniques including Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC) and cross-validation (CV) on standard datasets from the UCI and Delve repositories. We see that SRM induces omnivariate trees that have a small percentage of multivariate nodes close to the root and they generalize more or at least as accurately as those constructed using other model selection techniques.
