'Kernel methods' presentation slideshows

Ali Ghodsi Department of Statistics and Actuarial Science University of Waterloo October 2006

Learning from Shadows Dimensionality Reduction and its Application in Artificial Intelligence, Signal Processing and Robotics. Ali Ghodsi Department of Statistics and Actuarial Science University of Waterloo October 2006. Dimensionality Reduction. Dimensionality Reduction.

825 views • 65 slides

General setting

Text Mining, Information and Fact Extraction Part 4: Applications Marie-Francine Moens Department of Computer Science Katholieke Universiteit Leuven, Belgium sien.moens@cs.kuleuven.be. General setting.

622 views • 51 slides

Support Vector and Kernel Methods

Support Vector and Kernel Methods. John Shawe-Taylor University of Southampton. Motivation. Linear learning typically has nice properties Unique optimal solutions Fast learning algorithms Better statistical analysis But one big problem Insufficient capacity. Historical perspective.

546 views • 42 slides

VARs and factors

644 views • 38 slides

Lecture 1: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning. Isabelle Guyon isabelle@clopinet.com. What is Machine Learning?. Learning algorithm. Trained machine. TRAINING DATA. Answer. ?. Query. What for?. Classification Time series prediction Regression Clustering. Market Analysis.

615 views • 36 slides

Kernel Methods: Basics

Kernel Methods: Basics. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Outline. Kernel Methods: Basic ideas Kernels and similarity How to choose kernels? Kernels and learning. Kernel Methods: Basic ideas.

482 views • 35 slides

New Horizons in Machine Learning

New Horizons in Machine Learning. Avrim Blum CMU. This is mostly a survey, but last part is joint work with Nina Balcan and Santosh Vempala. [Workshop on New Horizons in Computing, Kyoto 2005]. What is Machine Learning?.

448 views • 31 slides

Diffusion Geometries in Document Spaces. Multiscale Harmonic Analysis.

Diffusion Geometries in Document Spaces. Multiscale Harmonic Analysis. R .R. Coifman, S. Lafon, A. Lee, M. Maggioni, B.Nadler. F. Warner, S. Zucker. Mathematics Department Program of Applied Mathematics.

416 views • 31 slides

Protein-Ligand Interaction Prediction: An Improved Chemogenomics Approach

Protein-Ligand Interaction Prediction: An Improved Chemogenomics Approach. Laurent Jacob Jean-Philippe Vert. Introduction. Predicting interactions between small molecules and proteins Vital to the drug discovery process Key to understanding biological processes. 3 classes of drug targets

506 views • 30 slides

Presented by: Peng Zhang 4/15/2011

Low-Rank Kernel Learning with Bregman Matrix Divergences Brian Kulis, Matyas A. Sustik and Inderjit S. Dhillon Journal of Machine Learning Research 10 (2009) 341-376. Presented by: Peng Zhang 4/15/2011. Outline. Motivation Major Contributions Preliminaries Algorithms Discussions

494 views • 28 slides

Measure Independence in Kernel Space

Measure Independence in Kernel Space. Presented by: Qiang Lou. References. I made slides based on following papers: F. Bach and M. Jordan. Kernel Independent Component Analysis. Journal of Machine Learning Research, 2002.

344 views • 26 slides

Graphical Multi-Task Learning

Graphical Multi-Task Learning. Dan Sheldon Cornell University NIPS SISO Workshop 12/12/2008. Multi-Task Learning (MTL). Separate but related learning tasks --- solve them jointly to achieve better performance

341 views • 23 slides

Kernel Methods for De-noising with Neuroimaging Application

Kernel Methods for De-noising with Neuroimaging Application. Trine Julie Abrahamsen September 8, 2009. Overview. Introduction Kernel Principal Component Analysis The Pre-image Problem Analysis of Cimbi Data Conclusions. Introduction.

384 views • 22 slides

Welcome to the Kernel-Club

Welcome to the Kernel-Club. Free dates: Oct.1, Oct.8 , Oct.15, Oct.29, Nov.12. Aim: Read papers on Kernels and discuss them. To do: We need to find a suitable paper for next week. every session has one chair (the one who really reads the paper).

190 views • 17 slides

Face Recognition based on Kernel Radial Basis Function

Neural Networks. Face Recognition based on Kernel Radial Basis Function. Ali Razmkhah Dr. Ebrahimi. 9 Jan 2010 Sahand University of Technology. Introduction. Face recognition is a very challenging research problem due to variations in

397 views • 17 slides

Missing at Random (MAR)

Missing at Random (MAR). ? is unknown parameter of the distribution for the missing-data mechanism The probability some data are missing does not depend on the actual values of the missing data

562 views • 16 slides

Introduction to Kernels

Introduction to Kernels. (chapters 1,2,3,4). Max Welling October 1 2004. Introduction. What is the goal of (pick your favorite name): - Machine Learning - Data Mining - Pattern Recognition - Data Analysis - Statistics

323 views • 16 slides

General Information

General Information. Course Id: COSC6342 Machine Learning Time: MO/WE 2:30-4p Instructor: Christoph F. Eick Classroom: SEC 201 E-mail: ceick@aol.com Homepage: http://www2.cs.uh.edu/~ceick/. What is Machine Learning?. Machine Learning is the

276 views • 16 slides

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

420 views • 15 slides

Kernel-class

Kernel-class. Jan. 13 2005. Recap : Feature Spaces. non-linear mapping to F 1. high-D space 2. infinite-D countable space : 3. function space (Hilbert space). example:. Recap : Kernel Trick. Note: In the dual representation we used the Gram matrix to express the solution.

187 views • 9 slides