Browsing by Subject "Discretization method"

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    Animation of deformable models
    (Pergamon Press, 1994) Güdükbay, Uğur; Özgüç, B.
    Although kinematic modelling methods are adequate for describing the shapes of static objects, they are insufficient when it comes to producing realistic animation. Physically based modelling remedies this problem by including forces, masses, strain energies and other physical quantities. The paper describes a system for the animation of deformable models. The system uses physically based modelling methods and approaches from elasticity theory for animating the models. Two different formulations, namely the primal formulation and the hybrid formulation, are implemented so that the user can select the one most suitable for an animation depending on the rigidity of the models. Collision of the models with impenetrable obstacles and constraining of the model points to fixed positions in space are implemented for use in the animations. © 1994.
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    A discretization method based on maximizing the area under receiver operating characteristic curve
    (World Scientific Publishing Co. Pte. Ltd., 2013) Kurtcephe, M.; Güvenir H. A.
    Many machine learning algorithms require the features to be categorical. Hence, they require all numeric-valued data to be discretized into intervals. In this paper, we present a new discretization method based on the receiver operating characteristics (ROC) Curve (AUC) measure. Maximum area under ROC curve-based discretization (MAD) is a global, static and supervised discretization method. MAD uses the sorted order of the continuous values of a feature and discretizes the feature in such a way that the AUC based on that feature is to be maximized. The proposed method is compared with alternative discretization methods such as ChiMerge, Entropy-Minimum Description Length Principle (MDLP), Fixed Frequency Discretization (FFD), and Proportional Discretization (PD). FFD and PD have been recently proposed and are designed for Naïve Bayes learning. ChiMerge is a merging discretization method as the MAD method. Evaluations are performed in terms of M-Measure, an AUC-based metric for multi-class classification, and accuracy values obtained from Naïve Bayes and Aggregating One-Dependence Estimators (AODE) algorithms by using real-world datasets. Empirical results show that MAD is a strong candidate to be a good alternative to other discretization methods. © 2013 World Scientific Publishing Company.