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The Netflix Challenge Parallel Collaborative Filtering

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.

By liam
(346 views)

Paul Kantor Rutgers May 14, 2007

Paul Kantor Rutgers May 14, 2007

Bayesian Models, Prior Knowledge, and Data Fusion for Monitoring Messages and Identifying Authors. Paul Kantor Rutgers May 14, 2007. Outline. The Team Bayes’ Methods Method of Evaluation A toy Example Expert Knowledge Efficiency issues Entity Resolution Conclusions. Many collaborator.

By arleen
(284 views)

Introduction to Machine Learning

Introduction to Machine Learning

Introduction to Machine Learning. Reading for today: R&N 18.1-18.4 Next lecture: R&N 18.6-18.12, 20.1-20.3.2. Outline. The importance of a good representation Different types of learning problems Different types of learning algorithms Supervised learning Decision trees Naïve Bayes

By ostinmannual
(305 views)

TrainSMART for Decision Makers

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:

By Antony
(327 views)

Machine Learning Chapter 3. Decision Tree Learning

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

By Rita
(341 views)

Conditional Random Fields model

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.

By mercia
(257 views)

CAP 5636 – Advanced Artificial Intelligence

CAP 5636 – Advanced Artificial Intelligence

CAP 5636 – Advanced Artificial Intelligence. Naïve Bayes. Instructor: Lotzi Bölöni [These slides were adapted from the ones created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley, available at http://ai.berkeley.edu.]. Machine Learning.

By mimis
(193 views)

Lecture 4: Smoothing for Language Models

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

By ham
(603 views)

Software Agents for Web Mining

Software Agents for Web Mining

Software Agents for Web Mining. FYP Project by: Shuchi Mittal Quek Siew Guat Patricia Professor: Franklin Fu. Organisation. Monitor/Retrieval. Crawling relevant URL’s Search for required data and links HTML parsing and cleaning Information Extraction Transfer raw data to the Database.

By kalin
(212 views)

Machine Learning – Classifiers and Boosting

Machine Learning – Classifiers and Boosting

Machine Learning – Classifiers and Boosting. Reading Ch 18.6-18.12, 20.1-20.3.2. Outline. Different types of learning problems Different types of learning algorithms Supervised learning Decision trees Naïve Bayes Perceptrons, Multi-layer Neural Networks Boosting

By ayanna
(454 views)

CS 1674: Intro to Computer Vision Visual Recognition

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

By shani
(83 views)

C4.5 Demo

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

By sarah
(195 views)

Distant Supervision for Knowledge Base Population

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)

By loren
(182 views)

Convex Point Estimation using Undirected Bayesian Transfer Hierarchies

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

By yaholo
(1 views)

Using Corpora for Language Research

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

By deepak
(117 views)

Lecture outline

Lecture outline

Lecture outline. Classification Naïve Bayes c lassifier Nearest-neighbor classifier. Eager vs Lazy learners. Eager learners: learn the model as soon as the training data becomes available Lazy learners: delay model-building until testing data needs to be classified

By faolan
(116 views)

Artificial Intelligence

Artificial Intelligence

Artificial Intelligence. Steven M. Bellovin. What is Artificial Intelligence?. You know what it is—computer programs that “think” or otherwise act “intelligent” The Turing test? What is “machine learning” (ML)?

By daphne
(88 views)

CS 2770: Computer Vision Intro to Visual Recognition

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)

By dash
(138 views)

Information Extraction on Real Estate Rental Classifieds

Information Extraction on Real Estate Rental Classifieds

Information Extraction on Real Estate Rental Classifieds. Eddy Hartanto Ryohei Takahashi. Overview. We want to extract 10 fields:. Security deposit Square footage Number of bathrooms Contact person’s name Contact phone number. Nearby landmarks Cost of parking Date available

By albert
(122 views)

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

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

By libitha
(107 views)

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