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Bayesian Networks Jay Gagnon Brian Keuling Bayesian Networks Directed Acyclic Graph (DAG) Nodes = variables Arcs = dependence relationship Conditional probability ...

APPLICATION OF BAYESIAN BELIEVE NETWORKS FOR CONTINUOUS RISK EVALUATION AND DECISION SUPPORT OF SAFETY MANAGEMENT IN MINING Todor P. Petrov University of Minig and ...

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Maximum Entropy deconvolution Another Bayesian technique Aim is to minimise a smoothness function proportional to the Shannon entropy, ...

Matlab Tutorial Course web page: www.cis.udel.edu/~cer/arv September 12, 2002 Announcements First paper presentations are allocated two students per paper Homework ...

P(D|h) = probability of observing data D given some world where hypothesis holds ... Just as in the KDD Cup example. Feature selection is required on your homework. 18 ...

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Wavelets: Motivation, Construction, & Application Jackie (Jianhong) Shen School of Math, University of Minnesota, Minneapolis www.math.umn.edu/~jhshen

For each category, compute a prototype vector by summing the vectors of the ... Assign test documents to the category with the closest prototype vector based on ...

Machine Learning

Matt VanLandeghem and Grant Sorensen Too many parameters: Lots of variance in predicted values Too few parameters: Missing important parameters Variance/bias tradeoff ...

Principles and Applications of Probabilistic Learning Padhraic Smyth Department of Computer Science University of California, Irvine www.ics.uci.edu/~smyth

sign. Neural Networks. Sigmoidal gates: a= xiwi and. output = 1/(1 e-a) ... Aim: Find a small decision tree with. a low observed error. Support Vector Machine ...

0 reconstructing evolutionary trees

machine learning Example: Learning to Play Checkers What is the experience? What exactly should be learned(knowledge type)?

Title: Notes 1: Introduction to Artificial Intelligence Author: Padhraic Smyth Last modified by: Hall, Laura Created Date: 9/23/1998 12:48:10 PM Document presentation ...

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Intelligent automated tools needed to discover relevant, useful, and interesting ... discovered rules to produce more intelligent system. 9/16/09. Thesis ...

CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly ...

2. lessens the 'brittleness' of the system. Using learning, the software agents can adapt to: ... the difference between. information needed to identify ...

Calculator. Recitation. What is meant by feasibility? What are three kinds of feasibility? ... Basic two types of contracts fixed price and cost reimbursement ...

Haplotype inference on pooled samples. The input contain n pools ... Need to calculate the probability of a pedigree on every haplotype configuration ...

Title: A Discussion of Some Intuitions of Defeasible Reasoning Author: ics Last modified by: Administrator Created Date: 5/4/2004 4:01:26 PM Document presentation format

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graph of coauthor links, 100k authors, 300k links. data over several years ... predict if pair(A,B) will coauthor. Training and test pairs selected in various ways ...

and more generally cost(i,j) is a matrix of K x K losses (e.g., surgery, spam email, etc) ... Requires fast lookup at run-time to do classification with large n ...

Ranking and Lift Curves ... n/r is the 'lift' provided by the classifier for top K ... Random ranking gives lift = 1, or 0% increase in lift ...

Discriminative classifiers. No probabilities. Learn the decision boundaries directly ... Discriminative models, focus on locating optimal decision boundaries ...

Tutorial 6 Bias and variance of estimators The score and Fisher information Cramer-Rao inequality Estimators and their Properties Let be a ...

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