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Nonlinear Dimensionality Reduction by Locally Linear Embedding

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Nonlinear Dimensionality Reduction by Locally Linear Embedding. Sam T. Roweis and Lawrence K. Saul. Presented by Yueng-tien , Lo. Reference: "Nonlinear dimensionality reduction by locally linear embedding," Roweis & Saul, Science, 2000.

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766 views • 30 slides
A Kernel Approach for Learning From Almost Orthogonal Pattern *

A Kernel Approach for Learning From Almost Orthogonal Pattern *

A Kernel Approach for Learning From Almost Orthogonal Pattern *. CIS 525 Class Presentation Professor: Slobodan Vucetic Presenter: Yilian Qin. * B. Scholkopf et al ., Proc. 13 th ECML , Aug 19-23, 2002, pp. 511-528. Presentation Outline. Introduction Motivation

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430 views • 26 slides
Reproducing Kernel Hilbert Space (RKHS), Regularization Theory, and Kernel Methods

Reproducing Kernel Hilbert Space (RKHS), Regularization Theory, and Kernel Methods

Reproducing Kernel Hilbert Space (RKHS), Regularization Theory, and Kernel Methods. Shaohua (Kevin) Zhou Center for Automation Research Department of Electrical and Computer Engineering University of Maryland, College Park. Overview. Reproducing Kernel Hilbert Space (RKHS)

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598 views • 20 slides
Pattern Recognition and Machine Learning: Kernel Methods

Pattern Recognition and Machine Learning: Kernel Methods

Pattern Recognition and Machine Learning: Kernel Methods. Overview. Many linear parametric models can be recast into an equivalent dual representation in which the predictions are based on linear combinations of a kernel function evaluated at the training data points

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431 views • 15 slides

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