PPT-1 Discretization

of Fluid Models Navier Stokes Dr Farzad Ismail School of Aerospace and Mechanical Engineering Universiti Sains Malaysia Nibong Tebal 14300 Pulau Pinang Week 5 Lecture 1 and 2 2 Preview. Consistency The discretization of a PDE should become exac t as the mesh size tends to zero truncation error should vanish 2 Stability Numerical errors which are generated during the solution of discretized equations should not be magni64257ed 3 Con Some Machine Learning algorithms require a discrete feature space but in realworld applications con tinuous attributes must be handled To deal with this problem many supervised discretization meth ods have been proposed but little has been done to s e presen t a comparison of three en trop ybased discretiza tion metho ds in a con text of learning classi cation rules W e compare the binary recursiv e discretization with a stopping criterion based on the Minim um Description Length Principle MDLP Rony. . Goldenthal. et al. . Presenter : . SoHyeon. . Jeong. . (SoHyeon.Jeong@gmail.com). ACM SIGGRAPH 2007. Movie. Contents. Abstract. Introduction. Related Work. Cloth Model. Constrained Dynamics. Analysis. Santiago González. <sgonzalez@fi.upm.es>. Contents. Introduction. . CRISP-DM (1). Tools . Data . understanding. Data . preparation. Modeling. (2). Association. rules. ? . Supervised. Case summary. First International High-Order . CFD Workshop. Jan. 7-8, 2012, Nashville, TN. Doru Caraeni, CD-Adapco.. Case 1.6 Vortex transport by uniform flow. Canonical test case to:. Assess efficiency of HO methods for LES/DES of turbulent flows,. Marvin L. Adams. Texas A&M University. CRASH Annual Review. Ann Arbor, MI. October 28-29, 2010. The integrated team has produced significant results this year.. Collaboration has been fruitful and essential.. Data Preparation & Preprocessing. Bamshad Mobasher. DePaul University. 2. The Knowledge Discovery Process. - The KDD Process. 3. Data Preprocessing. Why do we need to prepare the data?. In real world applications data can be . Jan Martin Nordbotten. Department of Mathematics, University of Bergen, Norway. Department of Civil and Environmental Engineering, Princeton University, USA. VISTA – Norwegian Academy of Sciences and Letters and Statoil ASA. Daisuke . Hotta. hotta@umd.edu. Advisor: Prof. Eugenia . Kalnay. Dept. of Atmospheric and Oceanic Science, . University of Maryland, College Park. ekalnay@atmos.umd.edu. Numerical Weather Prediction (NWP):. Kazhdan. [. Taubin. , 1995] . A Signal Processing Approach to Fair Surface . Design. [. Desbrun. , . et al.. , 1999] Implicit Fairing of Arbitrary Meshes…. [. Vallet. and Levy, 2008] . Spectral Geometry Processing with Manifold . Author: Daisuke . Hotta. hotta@umd.edu. Advisor: Prof. Eugenia . Kalnay. Dept. of Atmospheric and Oceanic Science, . University of Maryland, College Park. ekalnay@atmos.umd.edu. Numerical Weather Prediction (NWP):. . Ras. X. a. b. d. x1. 0.8. 2. 1. x2. 1. 0.5. 0. x3. 1.3. 3. 0. x4. 1.4. 1. 1. x5. 1.4. 2. 0. x6. 1.6. 3. 1. x7. 1.3. 1. 1. Quantization Process (based on . discernibility. formulas). V. a. = [0, 2) . What Is Data Mining?. Many people treat data mining as a synonym for another popularly used term, knowledge discovery from data, or KDD, while others view data mining as merely an essential step in the process of knowledge discovery. .