PDF-WOBBLED SYNTHETIC DISCRIMINANT FUNCTION CLASSIFICATION

ChyeHwa Loo and Atef Elsherbeni Center of Applied Electromagnetic Systems Research CAESR Electrical Engineering Department The University of Mississippi University MS 38677 ABSTRACT The task. Fisher Linear Discriminant 2 Multiple Discriminant Analysis brPage 2br CSE 555 Srihari 1 Motivation Projection that best separates the data in a least squares sense CA finds components that are useful for representing data owever no reason to assum Given . a quadratic equation use the . discriminant. to determine the nature . of the roots.. What is the discriminant?. The discriminant is the expression b. 2. – 4ac.. The value of the discriminant can be used. Why do we use the discriminant?. The discriminant tells us one of two things:. How many roots/x-intercepts/zeros does a quadratic function have?. How many solutions does a quadratic equation have?. Example. Rongcheng Lin. Computer Science Department. Contents. Motivation, Definition & Problem. Review of SVM. Hierarchical Classification. Path-based Approaches. Regularization-based Approaches. Motivation. Transductive. Regression Forests . Tsz-Ho. Yu. Danhang. . Tang. T-K. Kim. Sponsored by . 2. Motivation. Multiple cameras with invserse kinematics. [Bissacco et al. CVPR2007]. [Yao et al. IJCV2012]. Chye-Hwa Loo and Atef Elsherbeni Center of Applied Electromagnetic Systems Research (CAESR) Electrical Engineering Department The University of Mississippi, University, MS 38677 ABSTRACT The task Rongcheng Lin. Computer Science Department. Contents. Motivation, Definition & Problem. Review of SVM. Hierarchical Classification. Path-based Approaches. Regularization-based Approaches. Motivation. Linear Discriminant Analysis. Objective. -Project a . feature space (a dataset n-dimensional samples) onto a smaller . -Maintain . the . class separation. Reason. -Reduce computational costs. -Minimize . Nearest Neighbor Classification. Ashifur Rahman. About the Paper. Authors:. Trevor Hastie, . Stanford University. Robert . Tibshirani. , . University of Toronto. Publication:. KDD-1995. IEEE Transactions on Pattern Analysis and Machine Intelligence (1996). Recall, we have used the quadratic formula previously. Gives the location of the roots (x-intercepts) of the graph of a parabola. Function must be in standard form; f(x) = ax. 2. + . bx. + c. Example. Find the roots for the function f(x) = 2x. CS 560 Artificial Intelligence. Many slides throughout the course adapted from Svetlana . Lazebnik. , Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke . Zettlemoyer. , Rob . Pless. , Killian Weinberger, Deva . Mohammad Ali . Keyvanrad. Machine Learning. In the Name of God. Thanks to: . M. . . Soleymani. (Sharif University of Technology. ). R. . Zemel. (University of Toronto. ). p. . Smyth . (University of California, Irvine). Data Mining for Business Analytics. Shmueli. , Patel & Bruce. Discriminant Analysis: Background. A classical statistical technique. Used for classification long before data mining. Classifying organisms into species. Presenter: Syed Sharjeelullah. Course: CS-732. Authors: Jefferson L. P. Lima. David Macedo. . Cleber. . Zanchettin.