'Conditional independence assumptions' presentation slideshows

Lecture 5 : Classification (1)

836 views • 57 slides

CSCI 5582 Artificial Intelligence

CSCI 5582 Artificial Intelligence. Lecture 28 Jim Martin. HW 3. On the first set the average accuracy was 87% with 11 submissions at 100%. On the second set the average accuracy was 76% with 2 submissions getting 100% One of those was a rule-based approach

552 views • 47 slides

Bayesian Networks

Bayesian Networks. Read R&N Ch. 14.1-14.2 Next lecture: Read R&N 18.1-18.4. You will be expected to know. Basic concepts and vocabulary of Bayesian networks. Nodes represent random variables. Directed arcs represent (informally) direct influences.

453 views • 42 slides

Bayesian Networks

Bayesian Networks. CS171, Fall 2016 Introduction to Artificial Intelligence Prof. Richard Lathrop. Reading: R&N Ch 14. Why Bayesian Networks?. Knowledge Representation & Reasoning (Inference) Propositional Logic Knowledge Base : Propositional l ogic s entences

537 views • 26 slides

Summary

Interpretable Latent Feature Models For Text-Augmented Social Networks. James R. Foulds , Padhraic Smyth University of California, Irvine. The Nonparametric Latent Feature Relational Model (Miller et al., 2009) Actor i represented by a binary vector of features Z i

65 views • 1 slides