'Positive examples' presentation slideshows

Markov Logic in Natural Language Processing

Markov Logic in Natural Language Processing. Hoifung Poon Dept. of Computer Science & Eng. University of Washington. Overview. Motivation Foundational areas Markov logic NLP applications Basics Supervised learning Unsupervised learning. Languages Are Structural. governments

1.88k views • 158 slides

Ensembles for Cost-Sensitive Learning

Ensembles for Cost-Sensitive Learning. Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331 http://www.cs.orst.edu/~tgd. Outline. Cost-Sensitive Learning Problem Statement; Main Approaches Preliminaries Standard Form for Cost Matrices

945 views • 76 slides

Information Extraction and Integration

Information Extraction and Integration. Bing Liu Department of Computer Science University of Illinois at Chicago (UIC) liub@cs.uic.edu http://www.cs.uic.edu/~liub. Introduction. The Web is perhaps the single largest data source in the world. Much of the Web (content) mining is about

739 views • 55 slides

School-wide Positive Behavior Interventions and Support

School-wide Positive Behavior Interventions and Support. Rob Horner, George Sugai University of Oregon and University of Connecticut Hosted by CAL-STAT; Riverside County SELPA www.pbis.org www.swis.org. Goals.

620 views • 51 slides

Appeals to Emotion

Appeals to Emotion. These are devices that manipulate both conscious and unconscious preferences and biases. Often found in rhetoric (speeches), advertising , and political propaganda . Major Appeals to Emotion. Loaded Words Glittering Generalities Card Stacking Plain Folks Appeal

1.03k views • 39 slides

Machine Learning: finding patterns

Machine Learning: finding patterns. Outline. Machine learning and Classification Examples *Learning as Search Bias Weka. Finding patterns. Goal: programs that detect patterns and regularities in the data Strong patterns ? good predictions Problem 1: most patterns are not interesting

609 views • 39 slides

Using Weakly Labeled Data to Learn Models for Extracting Information from Biomedical Text

Using Weakly Labeled Data to Learn Models for Extracting Information from Biomedical Text. Mark Craven Department of Biostatistics & Medical Informatics Department of Computer Sciences University of Wisconsin U.S.A. craven@biostat.wisc.edu www.biostat.wisc.edu/~craven.

472 views • 37 slides

Cenni di Machine Learning: Decision Tree Learning

468 views • 34 slides

Reza Bosaghzadeh & Nathan Schneider LS2 ~ 1 December 2008

Unsupervised Approaches to Sequence Tagging, Morphology Induction, and Lexical Resource Acquisition. Reza Bosaghzadeh & Nathan Schneider LS2 ~ 1 December 2008. Unsupervised Methods. Sequence Labeling (Part-of-Speech Tagging) Morphology Induction Lexical Resource Acquisition. pronoun. verb.

458 views • 34 slides

Inductive Logic Programming: The Problem Specification

Inductive Logic Programming: The Problem Specification. Given : Examples: first-order atoms or definite clauses, each labeled positive or negative. Background knowledge: in the form of a definite clause theory. Language bias: constraints on the form of interesting new clauses.

429 views • 33 slides

Welcome

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Interpolants as Classifiers

Interpolants as Classifiers. Rahul Sharma Joint work with Aditya Nori (MSR India) and Alex Aiken (Stanford). Interpolants. If then an interpolant satisfies: contains only the variables common to and An interpolant is a simple proof

305 views • 22 slides

The Google Similarity Distance

The Google Similarity Distance. Presenter ? Chien-Hsing Chen Author: Rudi L. Cilibrasi Paul M.B. Vitanyi. 2007,TKDE. Outline. Motivation Objective NGD Experiments Conclusions Personal Opinion. Motivation.

209 views • 13 slides

331 Final

331 Final. Fall 2011. Details. 1:00-3:00 Wednesday 21 December FA 306 (this room!) Comprehensive with more emphasis on material since the midterm Look at the review page We may update this, so watch your email

207 views • 12 slides

Parenting=Major Influence

286 views • 10 slides

Multiplying and Dividing Integers

239 views • 8 slides

CS490D: Introduction to Data Mining Prof. Chris Clifton

CS490D: Introduction to Data Mining Prof. Chris Clifton. April 12, 2004 Multi-Relational Data Mining. What is MRDM?. Problem: Data in multiple tables Want rules/patterns/etc. across tables Solution: Represent as single table Join the data Construct a single view

209 views • 8 slides

Dancing & The Bible

306 views • 6 slides

136 views • 1 slides