'1 norm' presentation slideshows

? 5 ?? MATLAB ????

843 views • 74 slides

? 5 ?? MATLAB ????

897 views • 74 slides

Seeking Interpretable Models for High Dimensional Data

Seeking Interpretable Models for High Dimensional Data. Bin Yu Statistics Department, EECS Department University of California, Berkeley http://www.stat.berkeley.edu/~binyu. Basically, I am not interested in doing research and I never have been. I am interested in understanding,

573 views • 48 slides

Study of Sparse Classifier Design Algorithms

Study of Sparse Classifier Design Algorithms. Sachin Nagargoje, 08449 Advisor : Prof. Shirish Shevade 20 th June 2013. Outline. Introduction Sparsity w.r.t . features Using regularizer /penalty Traditional regularizer /penalty Other regularizer /penalty SparseNet

570 views • 45 slides

Arash Ali-Amini 1 Massoud BABAIE-ZADEH 1,2 Christian Jutten 2

618 views • 42 slides

Classification of Drugs by SVM

CZ3253: Computer Aided Drug design Lecture 7: Drug Design Methods II: SVM Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, National University of Singapore. Classification of Drugs by SVM.

588 views • 40 slides

Edge Preserving Image Restoration using L 1 norm

Edge Preserving Image Restoration using L 1 norm. Vivek Agarwal The University of Tennessee, Knoxville. Outline. Introduction Regularization based image restoration L 2 norm regularization L 1 norm regularization Tikhonov regularization Total Variation regularization

646 views • 40 slides

Fast, Small-Space Algorithms for Approximate Histogram Maintenance (on a Stream).

Fast, Small-Space Algorithms for Approximate Histogram Maintenance (on a Stream). A. Gilbert, S. Guha, P. Indyk, Y. Kotidis, S. Muthukrishnan, M. Strauss. A data stream. Data items/updates arrive one at a time Small storage, no random access to data unless stored.

261 views • 26 slides

Multiplicative updates for L1-regularized regression

Multiplicative updates for L1-regularized regression. Prof. Lawrence Saul Dept of Computer Science & Engineering UC San Diego (Joint work with Fei Sha & Albert Park ). Trends in data analysis. Larger data sets In 1990s : thousands of examples In 2000+ : millions or billions

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Multiple Kernel Learning

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Multiple Kernel Learning

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Multiple Kernel Learning

345 views • 24 slides

The Disputed Federalist Papers : SVM Feature Selection via Concave Minimization

The Disputed Federalist Papers : SVM Feature Selection via Concave Minimization. Glenn Fung and Olvi L. Mangasarian. CSNA 2002 June 13-16, 2002 Madison, Wisconsin. Outline of Talk. Support Vector Machines (SVM) Introduction. Standard Quadratic Programming Formulation.

370 views • 22 slides

Sparse Recovery Using Sparse (Random) Matrices

Sparse Recovery Using Sparse (Random) Matrices. Piotr Indyk MIT. Joint work with: Radu Berinde, Anna Gilbert, Howard Karloff, Martin Strauss and Milan Ruzic. Ax. A. =. x. x. x*. k=2. Linear Compression (a.k.a. linear sketching, compressed sensing). Setup:

322 views • 21 slides

Jun Ji Hansung University ( visiting the University of Texas at Austin )

Robust Inversion using Biweight norm. Jun Ji Hansung University ( visiting the University of Texas at Austin ). Contents. Introduction IRLS Review Robust norms and their weightings in IRLS l 1 norm Huber norm Hybrid norm Biweight norm Examples Conclusions. Introduction.

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Systems of Linear Equations

Systems of Linear Equations. Error Analysis and System Condition. Questions. What does it mean when a system of equations is ill-condition? How can we tell if a system is ill-condition? What can we expect from the solution when solving a system that is ill-condition?.

539 views • 18 slides

Addition and Subtraction of Vectors

Addition and Subtraction of Vectors. Lecture V1.1 Example 1 Moodle. Vectors have a magnitude and direction. Addition of Vectors. The resultant vector R = A + B. Shift B. Shift A. R = A + B = B + A. commutative law. A + B + C. D = A + B + C.

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Addition and Subtraction of Vectors

Addition and Subtraction of Vectors. Lecture V1.1 Example 1 Moodle. Vectors have a magnitude and direction. Addition of Vectors. The resultant vector R = A + B. Shift B. Shift A. R = A + B = B + A. commutative law. A + B + C. D = A + B + C.

282 views • 16 slides

Compressive Sampling

Compressive Sampling. Jan 25.2013 Pei Wu. Formalism. The observation y is linearly related with signal x: y=Ax Generally we need to have the number of observation no less than the number of signal. But we can make less observation if we know some property of signal. Sparsity.

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School Improvement Specialist Training August 3-4

School Improvement Specialist Training August 3-4. Norms and Protocols. NORMS. The standards of behavior by which we agree to operate while we are in this group. Every team needs to establish their own norms. NORM!. We need to greet our Norms with enthusiasm. DEVELOPING NORMS.

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