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randomforest-matlab

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  Analyzed about 1 year ago

This is a Matlab (and Standalone application) port for the excellent machine learning algorithm `Random Forests' - By Leo Breiman et al. from the R-source by Andy Liaw et al. http://cran.r-project.org/web/packages/randomForest/index.html ( Fortran original by Leo Breiman and Adele Cutler, R port by ... [More] Andy Liaw and Matthew Wiener.) Current code version is based on 4.5-29 from source of randomForest package. I especially am grateful for all the help i got from Andy Liaw. This project would not have been possible if not for the previous code by Andy Liaw, Matthew Wiener, Leo Brieman, Adele Cutler. The wiki has short articles on using rfImpute to input in missing values and basic installation procedures. 1-march-2010 Bug: Note the inputs to the package are in double. So make sure you are [Less]

4.96K lines of code

0 current contributors

about 4 years since last commit

2 users on Open Hub

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0.0
 
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mlpy - Machine Learning Python

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  Analyzed 8 months ago

74.7K lines of code

0 current contributors

about 5 years since last commit

1 users on Open Hub

Activity Not Available
5.0
 
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Licenses: No declared licenses

MLPACK C++ machine learning library

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  Analyzed 10 months ago

MLPACK is a fast C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum ... [More] flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression, least-angle regression, etc. [Less]

127K lines of code

41 current contributors

10 months since last commit

1 users on Open Hub

Activity Not Available
0.0
 
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Licenses: No declared licenses

multisearch-weka-package

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  Analyzed 2 days ago

Weka package for parameter optimization, similar to GridSearch.

4.08K lines of code

2 current contributors

12 months since last commit

1 users on Open Hub

Very Low Activity
0.0
 
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malibu

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  Analyzed about 1 year ago

malibu is a machine learning workbench written primarily in C++. It can handle a number of supervised learning problems including: classification, regression and multiple-instance learning. While malibu is targeted at remote Linux environments, it will compile on both Windows and Linux with minimal ... [More] effort using several compilers. Each algorithm in malibu compiles into a separate binary executable with a command-line interface and support for descriptive configuration files. Since algorithm parameters tend to overlap, it can output groups of parameters and supports several input configuration files. The learning algorithms in malibu support cluster computing as well as multiple CPUs/cores using the message passing interface (MPI). What makes malibu Different?malibu is primarily geared toward efficient learning in remote linux environments. It has a number of features that make it ideal in this situation: Configuration files Cluster computing through MPI Parses many dataset formats Supports command-line evaluation of learning Automated parameter selection SupportPlease report bugs and make feature requests on the Issues tab. Questions about installing and using malibu should be referred to the discussion board. Please restrict comments on the wiki to specific mistakes on the page. Inquiries about support services may be directed to Ezra on the discussion board LicensingThe malibu license is very similar to the BSD license (see license link for more details). Third-party algorithms each have their own license (see Installation for more details). Note that one third-party source file is included in the standard malibu source package because it is intrinsically required and it has its own license. [Less]

0 lines of code

0 current contributors

almost 8 years since last commit

0 users on Open Hub

Activity Not Available
0.0
 
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Mostly written in language not available
Licenses: BSD-4-Cla...

svdd-sh

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  Analyzed about 1 year ago

svdd-sh

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0 current contributors

about 8 years since last commit

0 users on Open Hub

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0.0
 
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Mostly written in language not available
Licenses: MIT

Datumbox Machine Learning Framework

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  Analyzed about 2 months ago

The Datumbox Machine Learning Framework is an open-source framework written in Java which allows the rapid development Machine Learning and Statistical applications. The main focus of the framework is to include a large number of machine learning algorithms & statistical tests and being able to handle medium-large sized datasets.

21.8K lines of code

0 current contributors

about 1 year since last commit

0 users on Open Hub

Activity Not Available
5.0
 
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Support Vector Machines Data Mining

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  No analysis available

The datamining Support Vector Machine (SVM) plug-in in MS SQL Server Analysis Services 2008. This plug-in is the SVM classification algorithm in addition to the shipped data mining algorithms with SQL Server.

0 lines of code

0 current contributors

0 since last commit

0 users on Open Hub

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0.0
 
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Mostly written in language not available
Licenses: No declared licenses

nieme

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  Analyzed about 1 year ago

Nieme is a machine learning library for large-scale classification, regression and ranking. It relies on the framework of energy-based models which unifies several learning algorithms. This framework also unifies batch and stochastic learning which are both seen as energy minimization problems. ... [More] Nieme is released under the GPL license. It is efficiently implemented in C++ and works on Linux, MacOS and Windows. Interfaces are available for C++, Java and Python. [Less]

0 lines of code

0 current contributors

over 8 years since last commit

0 users on Open Hub

Activity Not Available
0.0
 
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Mostly written in language not available
Licenses: GPL-3.0+