'Kernels' presentation slideshows

Machine Learning: k-Nearest Neighbor and Support Vector Machines

CMSC 471. Machine Learning: k-Nearest Neighbor and Support Vector Machines. skim 20.4, 20.6-20.7. Revised End-of-Semester Schedule. Wed 11/21 Machine Learning IV Mon 11/26 Philosophy of AI (You must read the three articles!) Wed 11/28 Special Topics Mon 12/3 Special Topics

755 views • 47 slides

Hierarchied Categories

Hierarchied Categories. Abelian categories, hierarchied abelian categories, graded hierarchied tensor categories. Abelian category.

588 views • 45 slides

Latent Semantic Kernels

689 views • 38 slides

4-H/FFA Crops Career Development Event 2011-2015

377 views • 26 slides

Non-Intrusive Dynamic Application Profiling for Detailed Loop Execution Characterization

Non-Intrusive Dynamic Application Profiling for Detailed Loop Execution Characterization . Ajay Nair, Roman Lysecky Department of Electrical and Computer Engineering University of Arizona Tucson, AZ USA {ajaynair, rlysecky}@ece.arizona.edu. Introduction Application Profiling.

339 views • 26 slides

Support Vector Machines

Support Vector Machines. Text Book Slides. Find a linear hyperplane (decision boundary) that will separate the data. Support Vector Machines. One Possible Solution. Support Vector Machines. Another possible solution. Support Vector Machines. Other possible solutions.

600 views • 25 slides

3D Seismic Imaging based on Spectral-element Simulations and Adjoint Methods

1 st QUEST Workshop, Sep 2010. 3D Seismic Imaging based on Spectral-element Simulations and Adjoint Methods. Qinya Liu Department of Physics University of Toronto. Collaborations with Carl Tape, Alessia Maggi, Jeroen Tromp, Dimitri Komatitsch and many others.

475 views • 24 slides

RTS: Kernel Design and Cyclic Executives

RTS: Kernel Design and Cyclic Executives. Chapter 4. Kernel & Device drivers. Servers (application ~, web ~, component ~). Shell. XWin. Thread lib. ftp. User applications. System call interface. Process, memory, file system, network managers. Kernel.

353 views • 22 slides

Section 3.4

Section 3.4. Additional Topics in Probability and Counting. Section 3.4 Objectives. Determine the number of ways a group of objects can be arranged in order Determine the number of ways to choose several objects from a group without regard to order

487 views • 20 slides

CUDA 5.0

CUDA 5.0. By Peter Holvenstot CS6260. CUDA 5.0. Latest iteration of CUDA toolkit Requires Compute Capability 3.0 Compatible Kepler cards being installed @WMU. Major New Features. GPUDirect Allows Direct Memory Access GPU Object Linking Libraries for GPU code Dynamic Parallelism

406 views • 18 slides

Nonparametric Methods: Nearest Neighbors

Nonparametric Methods: Nearest Neighbors. Oliver Schulte Machine Learning 726. Instance-based Methods. Model-based methods: estimate a fixed set of model parameters from data. compute prediction in closed form using parameters. Instance-based methods:

465 views • 17 slides

On Kernels, Margins, and Low-dimensional Mappings

On Kernels, Margins, and Low-dimensional Mappings. or Kernels versus features. Nina Balcan CMU Avrim Blum CMU Santosh Vempala MIT. Generic problem. Given a set of images: , want to learn a linear separator to distinguish men from women.

311 views • 17 slides

Kernel based data fusion

Kernel based data fusion. Discussion of a Paper by G. Lanckriet. Paper. Overview. Problem : Aggregation of heterogeneous data Idea : Different data are represented by different kernels Question : How to combine different kernels in an elegant/efficient way?

327 views • 17 slides

Sensitivity Kernels for Local Helioseismology

Sensitivity Kernels for Local Helioseismology. Aaron Birch NWRA, CoRA Division. Outline. Introduction to the forward problem Examples of kernels Artificial data Open questions. The Forward Problem. Given a model, e.g. sound-speed, flows want to know what to expect for measurements.

299 views • 16 slides

Secure Operating Systems

249 views • 11 slides

Whole Wheat Bread

By: Iman Aden and Harveen Dulai. Whole Wheat Bread. Plant source: Wheat Part of plant used: whole kernels of wheat grain . What is it made of?. Size: whole kernels are very small, about the size of very small seeds Wheat is tall, long grass (up to 2.1m) Characteristics:

989 views • 11 slides

An Execution Model for Heterogeneous Multicore Architectures

An Execution Model for Heterogeneous Multicore Architectures. Gregory Diamos, Andrew Kerr, and Sudhakar Yalamanchili Computer Architecture and Systems Laboratory Center for Experimental Research in Computer Systems School of Electrical and Computer Engineering Georgia Institute of Technology.

423 views • 10 slides

An Execution Model for Heterogeneous Multicore Architectures

An Execution Model for Heterogeneous Multicore Architectures. Gregory Diamos, Andrew Kerr, and Sudhakar Yalamanchili Computer Architecture and Systems Laboratory Center for Experimental Research in Computer Systems School of Electrical and Computer Engineering Georgia Institute of Technology.

209 views • 10 slides

Homomorphisms (11/20)

603 views • 4 slides

Performance of Graph and Biological Analytics on the IBM Cell Broadband Engine Processor

Performance of Graph and Biological Analytics on the IBM Cell Broadband Engine Processor. David A. Bader Tan M. Tran Georgia Institute of Technology. Biological Analytic: SSCA #1. Background and Intent.

151 views • 3 slides