'Training example' presentation slideshows

EM algorithm

EM algorithm. LING 572 Fei Xia 03/02/06. Outline. The EM algorithm EM for PM models Three special cases Inside-outside algorithm Forward-backward algorithm IBM models for MT. The EM algorithm. Basic setting in EM. X is a set of data points: observed data ? is a parameter vector.

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A Logic Based Classification Technique

A Logic Based Classification Technique. General-to-Specific Ordering. Logic Based. Like Decision Tree. Tree questions Sky? Sunny, ok, Wind? Strong, ok yes enjoy sport. Candidate Elimination. With candidate elimination object is to predict class through the use of expressions.

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Performing expressive music using Case-Based Reasoning

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Multiple features

Multiple features. Linear Regression with multiple variables. Machine Learning. Multiple features (variables). Multiple features (variables). Notation: = number of features = input (features) of training example. = value of feature in training example. Hypothesis:.

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Lecture 9 of 42

Lecture 9 of 42. Evaluation Functions and Expectiminimax Discussion: Games in AI. Wednesday, 13 September 2006 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: http://snipurl.com/v9v3

564 views • 24 slides

Engaging Your Students Through Narration

Engaging Your Students Through Narration. Rhonda D. Blackburn, PhD University of Texas at Dallas. Think!. Think of a course. How would you ground this in reality?. Objectives. Discuss how I improved my course using narrative course design. Redesign a course.

309 views • 22 slides

Support Vector Machines

Support Vector Machines. Perceptron Revisited: Linear Separators . Binary classification can be viewed as the task of separating classes in feature space:. w T x + b = 0. w T x + b > 0. w T x + b < 0. f ( x ) = sign( w T x + b ). Linear Separators.

889 views • 21 slides

Transfer in Reinforcement Learning via Markov Logic Networks

Transfer in Reinforcement Learning via Markov Logic Networks. Lisa Torrey, Jude Shavlik, Sriraam Natarajan, Pavan Kuppili, Trevor Walker University of Wisconsin-Madison, USA. Possible Benefits of Transfer in RL. Learning curves in the target task:. performance. with transfer.

437 views • 21 slides

SNoW: Sparse Network of Winnows

SNoW: Sparse Network of Winnows. Presented by Nick Rizzolo. Introduction. Multi-class classification Infinite attribute domain User configurable linear threshold unit networks Variety of update rules, algorithmic extensions. Outline. The Basic System Training Algorithmic Extensions

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Job / Tasks / Task Steps HITRA Transition and Communication Pack 2

Hazard Identification and Task Risk Assessment (HITRA). Job / Tasks / Task Steps HITRA Transition and Communication Pack 2. HITRA Transition and Communication Packs. BPXA HITRA Transition and Communication Packs

594 views • 8 slides