'Maximum margin' diaporamas de présentation

STATISTICAL LEARNING METHODS FOR MICROSTRUCTURES

STATISTICAL LEARNING METHODS FOR MICROSTRUCTURES. Veera Sundararaghavan and Prof. Nicholas Zabaras. Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H. T. Rhodes Hall Cornell University Ithaca, NY 14853-3801

1.35k views • 116 slides

Introduction to SVMs

Introduction to SVMs. SVMs. Geometric Maximizing Margin Kernel Methods Making nonlinear decision boundaries linear Efficiently! Capacity Structural Risk Minimization. Linear Classifiers. a. x. f. y est. f ( x , w ,b ) = sign( w . x - b ). denotes +1 denotes -1.

1k views • 85 slides

Introduction to SVMs

Introduction to SVMs. SVMs. Geometric Maximizing Margin Kernel Methods Making nonlinear decision boundaries linear Efficiently! Capacity Structural Risk Minimization. a. Linear Classifiers. x. f. y est. f ( x , w ,b ) = sign( w . x - b ). denotes +1 denotes -1.

745 views • 74 slides

Last lecture summary

Last lecture summary. Information theory. mathematical theory of the measurement of the information, quantifies information Information is inherently linked with uncertainty and surprise.

773 views • 67 slides

Machine Learning CS 165B Spring 2012

Machine Learning CS 165B Spring 2012. Course outline. Introduction (Ch. 1) Concept learning (Ch. 2) Decision trees (Ch. 3) Ensemble learning Neural Networks (Ch. 4) Linear classifiers Support Vector Machines Bayesian Learning (Ch. 6) Instance-based Learning Clustering

818 views • 66 slides

Support Vector Machines

Support Vector Machines. History. SVM is a classifier derived from statistical learning theory by Vapnik and Chervonenkis SVMs introduced by Boser, Guyon, Vapnik in COLT-92 Now an important and active field of all Machine Learning research.

591 views • 51 slides

Support Vector Machines

463 views • 33 slides

Survey of New York State Deer Hunters Following the 2009 Hunting Season

Survey of New York State Deer Hunters Following the 2009 Hunting Season. Jody W. Enck Human Dimensions Research Unit Department of Natural Resources Cornell University . Presentation Topics. Sampling considerations and survey design. Statistical precision. Survey topics:

774 views • 30 slides

Survey of New York State Deer Hunters Following the 2009 Hunting Season

Survey of New York State Deer Hunters Following the 2009 Hunting Season. Jody W. Enck Human Dimensions Research Unit Department of Natural Resources Cornell University . Presentation Topics. Sampling considerations and survey design. Statistical precision. Survey topics:

302 views • 30 slides

Supply Chain Management

Supply Chain Management. Lecture 26. Detailed Outline. Thursday April 22 Simulation game briefing Simulation game description FCQs Tuesday April 27 Review Simulation strategy Formula sheet (available online) Review final Thursday April 29 Simulation Game

2.46k views • 24 slides

Introduction to Support Vector Machines

250 views • 23 slides

Sketched Derivation of error bound using VC-dimension (1)

Sketched Derivation of error bound using VC-dimension (1). Bound our usual PAC expression by the probability that an algorithm has 0 error on the training examples S but high error on a second random sample , using a Chernoff bound:.

432 views • 20 slides

Support Vector Machines: A Survey

Support Vector Machines: A Survey. Qiang Yang, for ICDM 2006 Panel http://www.cse.ust.hk/~qyang Partially based on slides from Prof Andrew Moore at CMU: http://www.cs.cmu.edu/~awm/tutorials. Problem: Given a set of objects with known descriptive variables, and from known classes, i

138 views • 12 slides