'Conditional likelihood' presentation slideshows

Maxent Models and Discriminative Estimation

1.03k views • 74 slides

Maxent Models and Discriminative Estimation

960 views • 74 slides

Modeling and Forecasting Stock Return Volatility Using a Random Level Shift Model

692 views • 55 slides

Dynamic Bayesian Networks for Multimodal Interaction

Dynamic Bayesian Networks for Multimodal Interaction. Tony Jebara Machine Learning Lab Columbia University joint work with A. Howard and N. Gu. Outline. Introduction: Multi-Modal and Multi-Person Bayesian Networks and the Junction Tree Algorithm

588 views • 40 slides

Classification

Classification. Based in part on Chapter 10 of Hand, Manilla, & Smyth and Chapter 7 of Han and Kamber David Madigan. Predictive Modeling. Goal: learn a mapping: y = f ( x ; ? ) Need: 1. A model structure 2. A score function 3. An optimization strategy

509 views • 39 slides

Detecting Spatial Clustering in Matched Case-Control Studies

Detecting Spatial Clustering in Matched Case-Control Studies. Andrea Cook, MS Collaboration with: Dr. Yi Li November 4, 2004. Outline. Motivation Petrochemical exposure in relation to childhood brain and leukemia cancers Cumulative Geographic Residuals Unconditional Conditional

413 views • 31 slides

Sparse Word Graphs: A Scalable Algorithm for Capturing Word Correlations in Topic Models

Sparse Word Graphs: A Scalable Algorithm for Capturing Word Correlations in Topic Models. Ramesh Nallapati Joint work with John Lafferty, Amr Ahmed, William Cohen and Eric Xing Machine Learning Department Carnegie Mellon University. Introduction.

398 views • 28 slides

CS 480/680 : Intro to ML

CS 480/680 : Intro to ML. Lecture 0 3 : Logistic Regression. Outline. Announcements Bernoulli model Logistic regression Computation. Outline. Announcements Bernoulli model Logistic regression Computation. Announcements. Assignment 1 due in two weeks More questions on Piazza please

246 views • 24 slides

CS 480/680 : Intro to ML

CS 480/680 : Intro to ML. Lecture 0 4 : Logistic Regression. Outline. Announcements Bernoulli model Logistic regression Computation. Outline. Announcements Bernoulli model Logistic regression Computation. Announcements. Assignment 1 due next week. Outline. Announcements

250 views • 24 slides

Hidden Markov Models

Hidden Markov Models. Chris Holmes Professor of Biostatistics University of Oxford. Overview. Hidden Markov models (HMMs) are perhaps the most important models currently in use today in Bioinformatics Applications include

333 views • 19 slides

242 views • 11 slides