'Random forests' presentation slideshows

Apache Mahout Feb 13, 2012 Shannon Quinn

Cloud Computing CS 15-319. Apache Mahout Feb 13, 2012 Shannon Quinn. MapReduce Review. Scalable programming model Map phase Shuffle Reduce phase MapReduce Implementations Google Hadoop. chunks. C0. C1. C2. C3. Map Phase. M0. M1. M2. M3. mappers. IO0. IO1. IO2. IO3.

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H. John B. Birks 1,2,4 & Katherine J. Willis 1,3,4

ALPINES, TREES, AND REFUGIA. H. John B. Birks 1,2,4 & Katherine J. Willis 1,3,4 1 University of Bergen, 2 University College London, 3 University of Oxford, and 4 Jesus College Oxford. Introduction Definitions Last Glacial Maximum Alpines in LGM Trees in LGM

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Web-Mining Agents Ensemble Learning

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Combining models

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Statistics 202: Statistical Aspects of Data Mining Professor David Mease

Statistics 202: Statistical Aspects of Data Mining Professor David Mease Tuesday, Thursday 9:00-10:15 AM Terman 156 Lecture 13 = Finish Chapter 5 and Chapter 8 Agenda: 1) Reminder about 5 th Homework (due Tues 8/14 at 9AM ) 2) Discuss Final Exam

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Day 1_0Gy

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Machine Learning Lecture 8: Ensemble Methods

Machine Learning Lecture 8: Ensemble Methods. Moshe Koppel Slides adapted from Raymond J. Mooney and others. General Idea. Value of Ensembles.

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Using Random Forests Language Models in IBM RT-04 CTS

Using Random Forests Language Models in IBM RT-04 CTS. Peng Xu 1 and Lidia Mangu 2 1. CLSP, the Johns Hopkins University 2. IBM T.J. Waston Research Center March 24, 2005. n-gram Smoothing. Smoothing: take out some probability mass from seen n-grams and distribute among unseen n-grams

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Private Client-Side Profiling with Random Forests and Hidden Markov Models

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TapPrints : Your Finger Taps Have Fingerprints

TapPrints : Your Finger Taps Have Fingerprints. Presented by: Tom Staley. About. Paper by Emiliano Miluzzo Alexander Varshavsky Suhrid Balakrishnan Romit Roy Choudhury Originally presented at MobiSys2012, June 27, 2012. Introduction.

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Biomarker and Classifier Selection in Diverse Genetic Datasets

Biomarker and Classifier Selection in Diverse Genetic Datasets. University Of Connecticut 1 Department of Computer Science and Engineering 2 Department of Molecular and Cell Biology. James Lindsay 1 Ed Hemphill 2 Chih Lee 1 Ion Mandoiu 1 Craig Nelson 2.

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ICS 273A Intro Machine Learning

ICS 273A Intro Machine Learning. decision trees, random forests,. Decision Trees. Problem: decide whether to wait for a table at a restaurant, based on the following attributes : Alternate : is there an alternative restaurant nearby? Bar : is there a comfortable bar area to wait in?

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Online Machine Learning with Distributed In-memory Clusters

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Modeling the Human Classification of Galaxy Morphology

Modeling the Human Classification of Galaxy Morphology. Wednesday, December 5, 2007 Mike Specian. Galaxy Zoo Statistics. Site announced on July 15, 2007 Over 50,000 volunteers within first week Most galaxies classified 10 times or more More classifications = better data

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Random Forest Photometric Redshift Estimation

Random Forest Photometric Redshift Estimation. Samuel Carliles 1 Tamas Budavari 2 , Sebastien Heinis 2 , Carey Priebe 3 , Alex Szalay 2 Johns Hopkins University 1 Dept. of Computer Science 2 Dept. of Physics & Astronomy 3 Dept. of Applied Mathematics & Statistics. Photometric Redshifts.

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Facts About Random Forest - And Why They Matter

Random Forests are also very hard to beat in terms of performance. Of course, you can probably always find a model that can perform better, like a neural network, but these usually take much more time in the development.

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YKL REA Northern Pike Model

YKL REA Northern Pike Model. Photo: ADF&G. Fish Distribution Models. Process AFFID data for use in models. Classification tree and random forest models. ADF&G AFFID species occurrence data. Create stream network and landscape predictor variables in GIS. GIS source data.

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* Let x and y be vectors from reference and test images respectively Distance (Euclidean, Mahalanobis) based

Part 2: Change detection for SAR Imagery (based on Chapter 18 of the Book. Change-detection methods for location of mines in SAR imagery, by Dr. Nasser Nasrabadi, et al.). * Let x and y be vectors from reference and test images respectively

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51 Use Cases and implications for HPC & Apache Big Data Stack Architecture and Ogres

51 Use Cases and implications for HPC & Apache Big Data Stack Architecture and Ogres. Geoffrey Fox Judy Qiu Shantenu Jha (Rutgers) gcf@indiana.edu http://www.infomall.org School of Informatics and Computing Digital Science Center Indiana University Bloomington.

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