'Kernel matrix' presentation slideshows

Similarities, Distances and Manifold Learning

Similarities, Distances and Manifold Learning. Prof. Richard C. Wilson Dept. of Computer Science University of York. Background. Typically objects are characterised by features Face images SIFT features Object spectra ... If we measure n features ? n -dimensional space

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Classifying Images with Visual/Textual Cues

Classifying Images with Visual/Textual Cues. By Steven Kappes and Yan Cao. Motivation. Image search Building large sets of classified images Robotics. Background. Object recognition is unsolved Deformable shaped matching Classification based on purely textual information SVMs, PCA.

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Stoichiometric Analysis of Cellular Reaction Systems

Networks in Metabolism and Signaling Edda Klipp Humboldt University Berlin Lecture 5 / WS 2007/08 Stoichiometry in Metabolic Networks. Stoichiometric Analysis of Cellular Reaction Systems. F. E. 2D. 2A. B + C. v 1. v 3. v 2. G. http://www.genome.ad.jp/kegg/pathway/map/map01100.html.

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IMA, March 12, 2003 Gert Lanckriet ( gert@eecs.berkeley )

Learning the Kernel Matrix with Semi-Definite Programming. IMA, March 12, 2003 Gert Lanckriet ( gert@eecs.berkeley.edu ) Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, Michael Jordan U.C. Berkeley. Learning the Kernel Matrix with SDP. Machine learning. Kernel-based machine learning.

501 views • 34 slides

Classifiers

Classifiers. Given a feature representation for images, how do we learn a model for distinguishing features from different classes?. Decision boundary. Zebra. Non-zebra. Classifiers.

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Advanced TDDFT II

f xc. Advanced TDDFT II . II. Frequency-Dependent Kernels: Double Excitations . Neepa T. Maitra Hunter College and the Graduate Center of the City University of New York. Plan. -- Double-Excitations in TDDFT -- Autoionizing Resonances

471 views • 28 slides

Dimensionality reduction

Dimensionality reduction. Usman Roshan. Dimensionality reduction. What is dimensionality reduction? Compress high dimensional data into lower dimensions How do we achieve this?

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CS 59000 Statistical Machine learning Lecture 13

CS 59000 Statistical Machine learning Lecture 13. Yuan (Alan) Qi Purdue CS Oct. 8 2008. Outline . Review of kernel trick, kernel ridge regression and kernel Principle Component Analysis Gaussian processes (GPs) From linear regression to GP GP for regression. Kernel Trick.

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Learning the Kernel Matrix in Discriminant Analysis via QCQP

Learning the Kernel Matrix in Discriminant Analysis via QCQP. Jieping Ye Arizona State University. Joint work with Shuiwang Ji and Jianhui Chen. Kernel Discriminant Analysis. Regularized kernel discriminant analysis (RKDA) performs linear discriminant analysis (LDA) in the feature space.

533 views • 25 slides

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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Detecting Categories in News Video Using Image Features

Detecting Categories in News Video Using Image Features. Slav Petrov, Arlo Faria, Pascal Michaillat, Alex Berg, Andreas Stolcke, Dan Klein, Jitendra Malik. GB. SVM. Category correlation. Sequential context. Source combination. MFCC. GMM. 1-best selection. ASR. TFIDF. SVM.

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PARALLELIZED CONVOLUTION

PARALLELIZED CONVOLUTION. Convolution. Convolution is a mathematical operation on two functions A function derived from two given functions by integration that expresses how the shape of one is modified by the other. The Mathematical expression for basic two dimensional convolution is.

325 views • 18 slides

Kernel Properties

Kernel Properties. 2012 Computer Science PhD Showcase 17 February 2012 Roberto Valerio Dr. Ricardo Vilalta Pattern Analysis Lab. Kernel Properties. Agenda Introduction Objective Current work Experiments Conclusions Publications. Introduction. Machine Learning What is it?

333 views • 18 slides

A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts

A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts. Dhillon, Inderjit S., Yuqiang Guan, and Brian Kulis. K means and Kernel K means. Weighted Kernel k means. Matrix Form. Spectral Methods. Spectral Methods. Represented with Matrix. Ratio assoc. Ratio cut. L for Ncut.

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Learning a Kernel Matrix for Nonlinear Dimensionality Reduction

Learning a Kernel Matrix for Nonlinear Dimensionality Reduction. By K. Weinberger, F. Sha, and L. Saul Presented by Michael Barnathan. The Problem:. Data lies on or near a manifold . Lower dimensionality than overall space. Locally Euclidean.

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Presentation- Week 9

Presentation- Week 9. Maya Shoham. Baselines. Eth80 Database 30 Train, 50 Test 70.25% Accuracy The paper that used the eth80 database used all 400 pictures for training + test and got a best classification rate of 83%. Caltech101 Database 30 train, 1-50 test

141 views • 5 slides

The Chinese University of Hong Kong. Web Page Classification with Heterogeneous Data Fusion. Zenglin Xu, Irwin King and Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong { zlxu, king, lyu }@cse.cuhk.edu.hk. 1. Motivations. 2. Contributions.

166 views • 1 slides

Spectral Sequencing Based on Graph Distance

Spectral Sequencing Based on Graph Distance. Rong Liu, Hao Zhang, Oliver van Kaick {lrong, haoz, ovankaic} @ cs.sfu.ca. School of Computing Science, Simon Fraser University, Burnaby, Canada. Introduction. Problem.

113 views • 1 slides