Chapter 6: Kernel Methods

Jan 02, 2020

230 likes | 260 Vues

Chapter 6: Kernel Methods. Kernel regression and Local Regression Varying Coefficients Kernel Density estimation. Locally Weighted Regression. Local Polynomial Regression. Equivalent Kernels and DF. Conditional Plots and the Trellis Display. Kernel Density Classification.

Share Presentation

Embed Code

Link

issues
kernel regression
local regression
varying coefficients
kernel density classification
chapter 6 kernel methods

irush

Télécharger la présentation

Chapter 6: Kernel Methods

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Chapter 6: Kernel Methods • Kernel regression and Local Regression • Varying Coefficients • Kernel Density estimation

Locally Weighted Regression

Local Polynomial Regression
Equivalent Kernels and DF

Conditional Plots and the Trellis Display

Kernel Density Classification
Issues with model selection

Ch 2. Getting Started with the Kernel

Ch 2. Getting Started with the Kernel. A Beast of a Different Nature (1) . The kernel has several differences compared to normal user-space applications. The kernel does not have access to the C library. The kernel is coded in GNU C. The kernel lacks memory protection like user-space.

539 views • 16 slides

Linux Kernel Crash Dumps

Linux Kernel Crash Dumps. Matt D. Robinson and Tom Morano Silicon Graphics Computer Systems. Objectives LKCD Components Kernel Design Considerations Kernel Initiating Dumps Kernel Dumping Hooks/Execution Dump Initiation Code/Layout Dump Tunables Introduction to LCRASH.

885 views • 42 slides

Kernel Methods – Gaussian Processes

Kernel Methods – Gaussian Processes. Presented by Shankar Bhargav. Gaussian Processes. Extending role of kernels to probabilistic discriminative models leads to framework of Gaussian processes Linear regression model Evaluate posterior distribution over W

545 views • 15 slides

Kernel Methods

Kernel Methods. Lecture Notes for CMPUT 466/551 Nilanjan Ray. Kernel Methods: Key Points. Essentially a local regression (function estimation/fitting) technique Only the observations (training set) close to the query point are considered for regression computation

544 views • 20 slides

Kernel Methods Part 2

Kernel Methods Part 2. Bing Han June 26, 2008. Local Likelihood. Logistic Regression. Logistic Regression. After a simple calculation, we get We denote the probabilities Logistic regression models are usually fit by maximum likelihood. Local Likelihood.

359 views • 21 slides

Rakesh Babu 200402007 Advisors : Prof C. V. Jawahar, IIIT Hyderabad

Learning Non-Linear Kernel Combinations Subject to General Regularization: Theory and Applications. Rakesh Babu 200402007 Advisors : Prof C. V. Jawahar, IIIT Hyderabad Dr. Manik Varma, Microsoft Research. Overview. Introduction to Support Vector Machines (SVM)

994 views • 52 slides

Kernel Methods and SVM’s

Kernel Methods and SVM’s. Predictive Modeling. Goal: learn a mapping: y = f ( x ;  ) Need: 1. A model structure 2. A score function 3. An optimization strategy Categorical y  { c 1 ,…, c m } : classification Real-valued y : regression

297 views • 13 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.

380 views • 22 slides

Kernel Methods for De-noising with Neuroimaging Application

Kernel Methods for De-noising with Neuroimaging Application. Trine Julie Abrahamsen M.Sc. defense, August 10, 2009. Overview. Introduction Kernel Principal Component Analysis The Pre-image Problem Analysis of Cimbi Data Conclusions. Introduction.

362 views • 19 slides

Lecture 17: Supervised Learning Recap

Lecture 17: Supervised Learning Recap. Machine Learning April 6, 2010. Last Time. Support Vector Machines Kernel Methods. Today. Short recap of Kernel Methods Review of Supervised Learning Unsupervised Learning (Soft) K-means clustering Expectation Maximization Spectral Clustering

624 views • 45 slides

Lecture 7. Kernel Smoothing Methods

Lecture 7. Kernel Smoothing Methods. Instructed by Jinzhu Jia. Outline. Description of Kernel One-dimensional Kernel smoothing Selecting the Width of the Kernel Local Regression in R p Kernel Density Estimation and Classification. Nearest Neighbor Smoothing.

679 views • 27 slides

Machine Learning for Protein Classification: Kernel Methods

Machine Learning for Protein Classification: Kernel Methods. CS 374 Rajesh Ranganath 4/10/2008. Outline. Biological Motivation and Background Algorithmic Concepts Mismatch Kernels Semi-supervised methods. Proteins. The P rotein P roblem. Primary Structure can be easily determined

552 views • 30 slides

Neural Networks and Kernel Methods

Neural Networks and Kernel Methods. Generally, this will take a lot longer than 24 hours… We need to avoid doing this by hand!. How are we doing on the pass sequence?. We can now track both men, provided with Hand-labeled coordinates of both men in 30 frames

488 views • 40 slides

Kernel module programming and debugging

Kernel module programming and debugging. Advanced Operating Systems. Kernel Modules. The provision of kernel modules allows code to be introduced into a running kernel. This requires the kernel to be built with this capability, it also requires the commands

332 views • 21 slides

Overview of Kernel Methods (Part 2)

Overview of Kernel Methods (Part 2). Steve Vincent November 17, 2004. Overview. Kernel methods offer a modular framework. In a first step , a dataset is processed into a kernel matrix. Data can be of various types, and also heterogeneous types.

600 views • 37 slides

Kernel Timers

Kernel Timers. Goals . Learn how to study kernel code. Learn how kernel implements interval timers. Learn how signals work in Linux. Create a user-space mechanism to measure the execution time of a multithreaded program. Kernel Timers. Overview .

690 views • 42 slides

Kernel Methods: the Emergence of a Well-founded Machine Learning

Kernel Methods: the Emergence of a Well-founded Machine Learning. John Shawe-Taylor Centre for Computational Statistics and Machine Learning University College London. Overview. Celebration of 10 years of kernel methods: what has been achieved and what can we learn from the experience?

1.21k views • 95 slides

Machine Learning and Bioinformatics 機器學習與生物資訊學

Machine Learning and Bioinformatics 機器學習與生物資訊學. Enhancing instance-based classification using kernel density estimation. Outline. Kernel approximation Kernel a pproximation in O ( n ) Multi-dimensional kernel approximation Kernel d ensity e stimation

698 views • 55 slides

Comparing Kernel-based Learning Methods for Face Recognition

Comparing Kernel-based Learning Methods for Face Recognition. Zhiguo Li zhli@paul.rutgers.edu. Outline. Objective What is Kernel? Why Kernel? How to Kernel? Principal Component Analysis (PCA) vs. Fisher Discriminant Analysis (FDA) PCA vs. Kernel PCA (KPCA) Experiments

372 views • 12 slides

Kernel Methods: Support Vector Machines

Kernel Methods: Support Vector Machines. Maximum Margin Classifiers and Support Vector Machines. Generalized Linear Discriminant Functions. A linear discriminant function g(x) can be written as: g(x) = wo + Σ i w i x i i = 1, …, d (d is number of features).

326 views • 20 slides

Kernel Synchronization

Kernel Synchronization. 9662542 吳承峯 9662638 陳誌偉 9665516 劉銘傑. Kernel Synchronization. Think of the kernel as a server that answers requests. Requests can come either from a process running on a CPU or an external device issuing an interrupt request.

1.2k views • 97 slides

Setup and Compile Linux Kernel

Setup and Compile Linux Kernel. Speaker: Yi-Ji Jheng Date: 2007.10.31. Outline. Understand Linux Kernel Setup and Compile Linux Kernel Setup and Compile micro-Linux Kernel. UNDERSTAND LINUX KERNEL. Where is Kernel ?. User. Shell. Shell, KDE, AP. Kernel. Hardware. Kernel. Hardware.

511 views • 33 slides

More Related