'Local linear' diaporamas de présentation

Maclaurin and Taylor Polynomials

Maclaurin and Taylor Polynomials. Objective: Improve on the local linear approximation for higher order polynomials. Local Quadratic Approximations.

769 views • 55 slides

Maclaurin and Taylor Polynomials

Maclaurin and Taylor Polynomials. Objective: Improve on the local linear approximation for higher order polynomials. Local Quadratic Approximations.

978 views • 55 slides

Maclaurin and Taylor Polynomials

Maclaurin and Taylor Polynomials. Objective: Improve on the local linear approximation for higher order polynomials. Local Quadratic Approximations.

671 views • 55 slides

Yuanlu Xu

20 12. Moving Object Segmentation by Pursuing Local Spatio-Temporal Manifolds. Yuanlu Xu. Problem. Segmenting moving f oreground in a video. Related work & intuitions. Dynamic background ~ dynamic textures. Image sequences of certain textures moving and changing under certain properties.

762 views • 53 slides

Analysis of Microarray Data using Monte Carlo Neural Networks

Analysis of Microarray Data using Monte Carlo Neural Networks. Jeff Knisley, Lloyd Lee Glenn, Karl Joplin, and Patricia Carey The Institute for Quantitative Biology East Tennessee State University. Outline of Talk. Microarray Data Neural Networks A Simple Perceptron Example

688 views • 52 slides

J. A. Valdivia

Modeling the multivariate dynamical properties of spatially distributed data sets: the electrojet currents. J. A. Valdivia. Collaborators A. Klimas D. Vassiliadis S. Sharma K. Papadopoulos. Outline. Magnetospheric activity As an input-output system Time delay embeddings

566 views • 47 slides

Part 3: Image Classification using Sparse Coding: Advanced Topics

Part 3: Image Classification using Sparse Coding: Advanced Topics. Kai Yu Dept. of Media Analytics NEC Laboratories America. Andrew Ng Computer Science Dept. Stanford University. Outline of Part 3. Why can sparse coding learn good features?

1.12k views • 41 slides

Machine Learning 21431

612 views • 36 slides

Machine Learning 21431

366 views • 36 slides

Introduction to Chaos by: Saeed Heidary 29 Feb 2013

Introduction to Chaos by: Saeed Heidary 29 Feb 2013. Outline:. Chaos in Deterministic Dynamical systems Sensitivity to initial conditions Lyapunov exponent Fractal geometry Chaotic time series prediction. Chaos in Deterministic Dynamical systems.

450 views • 25 slides