'Maximum likelihood parameter estimation' presentation slideshows

Andrew Blake, Microsoft Research and Bill Freeman, MIT, ICCV 2003 and Andrew W. Moore, Carnegie Mellon University

Introduction to Expectation Maximization Assembled and extended by Longin Jan Latecki Temple University, latecki@temple.edu based on slides by. Andrew Blake, Microsoft Research and Bill Freeman, MIT, ICCV 2003 and Andrew W. Moore, Carnegie Mellon University.

1.04k views • 75 slides

Intro to Machine Learning

725 views • 52 slides

Generative (Bayesian) modeling

Generative (Bayesian) modeling. 04/04/2016. Slides by (credit to ): David M. Blei Andrew Y. Ng, Michael I. Jordan , Ido Abramovich , L . Fei -Fe i, P . Perona , J . Sivic , B . Russell, A. Efros , A . Zisserman , B . Freeman , Tomasz Malisiewicz , Thomas Huffman,

466 views • 46 slides

Generative (Bayesian) modeling

Generative (Bayesian) modeling. 06/04/2016. Slides by (credit to ): David M. Blei Andrew Y. Ng, Michael I. Jordan , Ido Abramovich , L . Fei -Fe i, P . Perona , J . Sivic , B . Russell, A. Efros , A . Zisserman , B . Freeman , Tomasz Malisiewicz , Thomas Huffman,

455 views • 45 slides

Speech Recognition

Speech Recognition. Pattern Classification. Pattern Classification. Introduction Parametric classifiers Semi-parametric classifiers Dimensionality reduction Significance testing. Classifier. Feature Extraction. Class ? i. Feature Vectors x. Observation s. Pattern Classification.

425 views • 42 slides

Speech Recognition

Speech Recognition. Pattern Classification. Pattern Classification. Introduction Parametric classifiers Semi-parametric classifiers Dimensionality reduction Significance testing. Classifier. Feature Extraction. Class ? i. Feature Vectors x. Observation s. Pattern Classification.

563 views • 40 slides

Speech Recognition

Speech Recognition. Pattern Classification. Pattern Classification. Introduction Parametric classifiers Semi-parametric classifiers Dimensionality reduction Significance testing. Classifier. Feature Extraction. Class ? i. Feature Vectors x. Observation s. Pattern Classification.

419 views • 40 slides

Linear Methods for Classification

Linear Methods for Classification. Lecture Notes for CMPUT 466/551 Nilanjan Ray. Linear Classification. What is meant by linear classification? The decision boundaries in the in the feature (input) space is linear Should the regions be contiguous?. R 1. R 2. X 2. R 3. R 4. X 1.

745 views • 36 slides

Linear Methods for Classification

Linear Methods for Classification. Lecture Notes for CMPUT 466/551 Nilanjan Ray. Linear Classification. What is meant by linear classification? The decision boundaries in the in the feature (input) space is linear Should the regions be contiguous?. R 1. R 2. X 2. R 3. R 4. X 1.

442 views • 27 slides

LECTURE 06: MAXIMUM LIKELIHOOD AND BAYESIAN ESTIMATION

560 views • 23 slides

Speaker Recognition

Speaker Recognition. S. Arun Nair, Vaibhav Singh, Dheeraj Mehra , Rohan Paul. Speaker Identification System. Enrollment Phase. Identification Phase. Methodology. Database Creation 30 speaker database Random text : 1 min samples Telephone Quality : 8bit samples at 8K rate

238 views • 22 slides

ECE 533 Project

354 views • 15 slides

231 views • 8 slides