'Training data' presentation slideshows

CS 1674: Intro to Computer Vision Visual Recognition

CS 1674: Intro to Computer Vision Visual Recognition. Prof. Adriana Kovashka University of Pittsburgh February 27, 2018. Plan for today. What is recognition? a.k.a. classification, categorization Support vector machines Separable case / non-separable case

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CS 2770: Computer Vision Intro to Visual Recognition

CS 2770: Computer Vision Intro to Visual Recognition. Prof. Adriana Kovashka University of Pittsburgh February 13, 2018. Plan for today. What is recognition? a.k.a. classification, categorization Support vector machines Separable case / non-separable case Linear / non-linear (kernels)

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Introduction to Machine Learning

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Lecture 4: Smoothing for Language Models

Lecture 4: Smoothing for Language Models. CSCI 544: Applied Natural Language Processing Nanyun (Violet) Peng. based on slides of Jason Eisner. Never trust a sample under 30. 20. 200. 2000000. 2000. Never trust a sample under 30. Smooth out the bumpy histograms to look more like the truth

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Conditional Random Fields model

Conditional Random Fields model. QingSong.Guo. Recent work. XML keyword query refinement Two ways: Focus on XML tree structure Focus on keywords. XML tree. In keyword query, there are many nodes in the XML tree matching the keywords.

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Artificial Intelligence

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Using Corpora for Language Research

Using Corpora for Language Research. COGS 523-Lecture 3 Corpus Annotation. Related Readings. Course Pack: Meyer (2002) Ch4; Sampson and McCarthy (2005) Ch 39; Garside (1997) Chs 4,5,16 Optional: McEnery et al (2006): A3, A4, A8, A9

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616 views • 41 slides

Paul Kantor Rutgers May 14, 2007

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Machine Learning Chapter 3. Decision Tree Learning

Machine Learning Chapter 3. Decision Tree Learning. Tom M. Mitchell. Abstract. Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting. Decision Tree for PlayTennis. A Tree to Predict C-Section Risk. Learned from medical records of 1000 women

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The Netflix Challenge Parallel Collaborative Filtering

The Netflix Challenge Parallel Collaborative Filtering. James Jolly Ben Murrell CS 387 Parallel Programming with MPI Dr. Fikret Ercal. What is Netflix?. subscription-based movie rental online frontend over 100,000 movies to pick from 8M subscribers 2007 net income: $67M.

610 views • 23 slides

Information Extraction on Real Estate Rental Classifieds

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Convex Point Estimation using Undirected Bayesian Transfer Hierarchies

Convex Point Estimation using Undirected Bayesian Transfer Hierarchies. Gal Elidan, Ben Packer, Geremy Heitz, Daphne Koller Computer Science Dept. Stanford University UAI 2008. Presented by Haojun Chen August 1 st , 2008. Outline. Background and motivation Undirected transfer hierarchies

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Lecture outline

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TrainSMART for Decision Makers

TrainSMART for Decision Makers. TrainSMART for Decision Makers. Goal of this training: To prepare I-TECH decision makers to rollout TrainSMART in their country project. TrainSMART for Decision Makers. Training Objectives: At the end of this training, participants will be able to:

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Wednesday, November 15, 2000 Cecil P. Schmidt Department of Computer Information Sciences, KSU http://www.cis.ksu.edu/~c

KDD Group Presentation. Instance-Based Learning. Wednesday, November 15, 2000 Cecil P. Schmidt Department of Computer Information Sciences, KSU http://www.cis.ksu.edu/~cps4444. Presentation Outline. What is Instance Based Learning? k -Nearest Neighbor Learning

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C4.5 Demo

C4.5 Demo. Andrew Rosenberg CS4701 11/30/04. What is c4.5?. c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. Running c4.5. On cunix.columbia.edu

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Software Agents for Web Mining

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Distant Supervision for Knowledge Base Population

Distant Supervision for Knowledge Base Population. Mihai Surdeanu, David McClosky , John Bauer, Julie Tibshirani , Angel Chang, Valentin Spitkovsky, Christopher Manning. Definition and Approach. We took part in TAC KBP 2010 this year (both tasks)

275 views • 5 slides