Random forest

Results: 386



#Item
321Missing data / Random forest / Variance / Matrix / Statistics / Data analysis / Imputation

Package ‘missForest’ July 2, 2014 Type Package Title Nonparametric Missing Value Imputation using Random Forest Version 1.4 Date[removed]

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Source URL: cran.r-project.org

Language: English - Date: 2014-07-02 12:27:17
322Artificial intelligence / Cluster analysis / Ensemble learning / Formal sciences / Statistical classification / Random forest / Data mining / Principal component analysis / Consensus clustering / Statistics / Machine learning / Data analysis

Package ‘symbolicDA’ July 2, 2014 Title Analysis of symbolic data Version[removed]Date[removed]Author

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Source URL: cran.r-project.org

Language: English - Date: 2014-07-02 16:41:17
323Artificial intelligence / Data mining / Ensemble learning / Decision tree learning / Machine learning / Random forest / Regression analysis / Decision trees / Statistics / Computational statistics

Package ‘evtree’ October 15, 2014 Title Evolutionary Learning of Globally Optimal Trees Version[removed]Date[removed]Description Commonly used classification and regression tree methods like the CART algorithm

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Source URL: cran.r-project.org

Language: English - Date: 2014-10-15 07:16:42
324Machine learning / Computational statistics / Regression analysis / Bootstrap aggregating / Random forest / Linear discriminant analysis / Leo Breiman / Cross-validation / Bootstrapping / Statistics / Statistical classification / Ensemble learning

New URL: http://www.R-project.org/conferences/DSC[removed]Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC[removed]March 20–22, Vienna, Austria ISSN 1609-395X

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Source URL: www.r-project.org

Language: English - Date: 2014-02-10 04:17:14
325Random sample / Stratified sampling / Standard error / Sample size determination / Diameter tape / Forest inventory / Proportionator / Statistics / Sampling / Timber cruise

WO AMENDMENT[removed] EFFECTIVE DATE: [removed] DURATION: This amendment is effective until superseded or removed. 2409.12_40

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Source URL: fs.usda.gov

Language: English - Date: 2012-06-19 13:28:16
326Measurement / Wood / Biomass / Renewable energy / Lumber / Carbon / Cubic foot / Chemistry / Matter / Bioenergy

Monitoring and Measuring Wood Carbon1 Neil Sampson2 The use of permanent sample plots is the most common method of evaluating changes in forest conditions. The plots are usually located through a stratified random sampli

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Source URL: www.sampsongroup.com

Language: English - Date: 2003-01-20 10:20:14
327Decision trees / Light sources / Astrophysics / Statistical classification / Machine learning / Supernova / Support vector machine / Decision tree learning / Random forest / Astronomy / Supernovae / Statistics

Object Classification at the Nearby Supernova Factory S. Bailey,1⋆ , C. Aragon,1 , R. Romano,1,2, R. C. Thomas,1 , B. A. Weaver1,3, and D. Wong1 1 2 3

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Source URL: faculty.washington.edu

Language: English - Date: 2008-07-07 20:08:50
328Artificial intelligence / Ensemble learning / Learning / Random forest / Classifier / Binary classification / Support vector machine / Statistics / Statistical classification / Machine learning

Combining Randomization and Discrimination for Fine-Grained Image Categorization Bangpeng Yao*, Aditya Khosla* and Li Fei-Fei The Vision Lab Computer Science Dept.

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Source URL: people.csail.mit.edu

Language: English - Date: 2011-07-10 06:50:12
329Statistical classification / Decision trees / Ensemble learning / Computer vision / Random forest / Feature selection / Object recognition / Decision tree learning / Segmentation / Statistics / Machine learning / Artificial intelligence

Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities Aditya Khosla, Bangpeng Yao and Li Fei-Fei 1 Introduction

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Source URL: people.csail.mit.edu

Language: English - Date: 2014-02-01 15:26:45
330Exoplanetology / Kepler mission / Space telescopes / Ensemble learning / Discovery program / Kepler / Random forest / Spacecraft / Spaceflight / Space technology

Jon Jenkins1,3, Sean McCauliff2,3, Joe Catanzarite1,3, Joe Twicken1,3, and Jennifer Campbell2,3 1SETI Institute, 2Orbital Sciences corporation, 3NASA Ames Research Center 4. New Results for Q1-Q16

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Source URL: nexsci.caltech.edu

Language: English - Date: 2013-11-22 11:13:14
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