'Pattern classification' diaporamas de présentation

Spoken Dialog Systems and Voice XML : Intro to Pattern Recognition

1.2k views • 95 slides

EE 7730: Image Analysis I

1.08k views • 76 slides

Chapter 10 Unsupervised Learning & Clustering

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 10 Unsupervised Learning & Clustering . Introduction

1.24k views • 67 slides

Phantom Limb Phenomena

Phantom Limb Phenomena. Hand movement observation by individuals born without hands: phantom limb experience constrains visual limb perception. Funk M , Shiffrar M , Brugger P .

1.17k views • 55 slides

Protein Evolution: SARS coronavirus as an example

Lecture 3: Protein Families and Family Prediction Methods Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, National University of Singapore. Protein Evolution: SARS coronavirus as an example. SARS Coronavirus.

605 views • 45 slides

Introduction to Neural Networks Computing

Introduction to Neural Networks Computing. CMSC491N/691N, Spring 2001. Notations. units: activation/output: if is an input unit, for other units , where f( .) is the activation function for weights: from unit i to unit j (other books use ).

478 views • 36 slides

Introduction to Neural Networks Computing

Introduction to Neural Networks Computing. CMSC491N/691N, Spring 2001. Notations. units: activation/output: if is an input unit, for other units , where f( .) is the activation function for weights: from unit i to unit j (other books use ).

440 views • 36 slides

Chapter 1: Introduction to Pattern Recognition (Sections 1.1-1.6)

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 1: Introduction to Pattern Recognition (Sections 1.1-1.6).

521 views • 31 slides

Chapter 3: Maximum-Likelihood and Bayesian Parameter Estimation (part 2)

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 3: Maximum-Likelihood and Bayesian Parameter Estimation (part 2).

911 views • 31 slides

Chapter 6: Multilayer Neural Networks (Sections 6.1-6.3)

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 6: Multilayer Neural Networks (Sections 6.1-6.3). Introduction

610 views • 31 slides

Introduction to Neural Networks Andy Philippides

Introduction to Neural Networks Andy Philippides Centre for Computational Neuroscience and Robotics (CCNR) School of Cognitive and Computing Sciences/School of Biological Sciences andrewop@cogs.susx.ac.uk Spring 2003. Lectures -- 2 per week Time Day Place

516 views • 31 slides

Methods of Deriving Biometric ROC Curves from the k -NN Classifier

Methods of Deriving Biometric ROC Curves from the k -NN Classifier. Robert S. Zack May 8, 2010. Agenda. Introduction to ROC Curves Classification Multi-Class Issues and Solutions New Derivation Methods Weak and Strong System Training Use Cases

471 views • 26 slides

A New Current Pattern Recognizing Protection Technique for High Impedance Fault Using Cross Correlation

A New Current Pattern Recognizing Protection Technique for High Impedance Fault Using Cross Correlation. A. M. Sharaf, Senior Member, IEEE University of New Brunswick, Fredericton, NB, Canada. M. M. Eissa, Senior Member, IEEE Faculty of Engineering, Helwan University, EGYPT.

401 views • 22 slides

Acoustic Emission Education Academic Program Dr. Boris Muravin, Prof. Gregory Muravin, Dr. Ludmila Lezvinsky

552 views • 17 slides

Predicting NFL Game Outcomes: Back-Propagating MLP

Predicting NFL Game Outcomes: Back-Propagating MLP. By Paul McBride. Project Goal. To predict the outcome of NFL games. Remove human bias Create a completely objective and statistics based prediction method. Why a back-propagating MLP?.

208 views • 10 slides

Fig. 4.5

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Fig. 4.5.

375 views • 9 slides

If P( ? m | x) ? 1 , then the nearest neighbor selection is almost always the same as the Bayes selection

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher.

222 views • 8 slides

If P( ? m | x) ? 1 , then the nearest neighbor selection is almost always the same as the Bayes selection

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher.

221 views • 8 slides

Painting Classification by Artist and Period Using Neural Network Pattern Classification Techniques

Painting Classification by Artist and Period Using Neural Network Pattern Classification Techniques Stuart Rowan 12/12/2008 Motivation Pattern classification techniques can be used to group paintings together It is desirable to group paintings by artist to detect forgeries

781 views • 6 slides

13. Component Analysis (also Chap 3)

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. 13. Component Analysis (also Chap 3).

256 views • 5 slides