Projects tagged ‘classification’

Divmod Reverend

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Analyzed about 7 years ago

Reverend is a general purpose Bayesian classifier written in Python, named after Rev. Thomas Bayes.

851 lines of code

0 current contributors

over 8 years since last commit

4 users on Open Hub

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0 Reviews

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Mostly written in Python

Licenses: LGPL

OpenIMAJ is a collection of libraries for multimedia analysis written in the Java programming language. OpenIMAJ contains classes that can perform processing, analysis and content-creation of many kinds of multimedia data, including images, video, audio and text. OpenIMAJ also incorporates a number ... [More]

778K lines of code

4 current contributors

16 days since last commit

3 users on Open Hub

Low Activity

0 Reviews

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Mostly written in Java

Licenses: BSD-3-Clause

Tags analytics audio-analysis classification computer-vision data-analysis data-mining education feature-detection feature-extraction hadoop image-analysis image-processing 8 more...

Sorted Pulse Data Library (SPDLib)

Analyzed about 10 hours ago

A library of tools and API (C++ and Python) for the storage and processing of large 3D laser scanning (LiDAR, ALS, TLS) datasets using a pulse based spatially indexed file format (SPD) which support for both discrete return and full waveform datasets.

210K lines of code

2 current contributors

about 2 months since last commit

2 users on Open Hub

Low Activity

0 Reviews

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Mostly written in C++

Licenses: GPL-3.0, MIT

Tags airborne als c++ classification cloud decomposition full full-waveform idl laser lidar lidardata 9 more...

MALLET

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Analyzed about 1 hour ago

MALLET (A Machine Learning for Language Toolkit) is an integrated collection of Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text

91.9K lines of code

0 current contributors

about 4 years since last commit

2 users on Open Hub

Inactive

0 Reviews

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Mostly written in Java

Licenses: CPL-1.0

Tags algorithms classification clustering crf framework hidden_markov_model hmm java machine_learning optimization topic_model

Java Data Mining Package (JDMP)

Analyzed about 12 hours ago

The Java Data Mining Package (JDMP) is an open source Java library for data analysis and machine learning. It facilitates the access to data sources and machine learning algorithms (e.g. clustering, regression, classification, graphical models, optimization) and provides visualization modules. It ... [More]

40.7K lines of code

0 current contributors

over 2 years since last commit

2 users on Open Hub

Inactive

0 Reviews

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Mostly written in Java

Licenses: LGPL-3.0

Tags algorithms analysis artificial_intelligence classification classifiers clustering data data_analysis data_mining distributed intelligent_data_analysis java 11 more...

fast-random-forest

Analyzed almost 2 years ago

What is FastRandomForest?FastRandomForest is a re-implementation of the Random Forest classifier (RF) for the Weka environment that brings speed and memory use improvements over the original Weka RF. Speed gains depend on many factors, but a 5-10x increase over Weka 3-6-1 on a quad core computer ... [More] is not uncommon, along with a 1.5x reduction in memory use. For detailed tests of speed and classification accuracy, as well as description of optimizations in the code, please refer to the FastRandomForest wiki at http://code.google.com/p/fast-random-forest/w or email the author at fran.supek\AT\irb.hr. Unrelated to the FastRF project, an MPI-enabled version of the Random Forest algorithm written in Fortran 90 is available from http://parf.googlecode.com. LicenseThis program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. Using from own Java codeJust add FastRandomForest.jar to your Java VM classpath by using the -cp switch, or by changing project dependencies in NetBeans/Eclipse/whatever IDE you use. Then use hr.irb.fastRandomForest.FastRandomForest as you would use any other classifier, see instructions at the WekaWiki: http://weka.sourceforge.net/wiki/index.php/Use_Weka_in_your_Java_code Using from Weka Explorer or Experimenter (versions 3.7.0, 3.6.1, 3.5.7 or earlier)1. Add the FastRandomForest.jar to your Java classpath when starting Weka. This is normally done by editing the line beginning with “cp=” in “RunWeka.ini” If "cp=" doesn't exist, search for "cmd_default=" and add after "#wekajar#;". 2. You need to extract the “GenericPropertiesCreator.props” file from your weka.jar (jar files are in fact ordinary zip archives, the GenericPropertiesCreator.props is under /weka/gui). 3. Place the file you've just extracted into the directory where you have installed Weka (on Windows this is commonly "C:\Program Files\Weka-3-6") 4. Under the # Lists the Classifiers-Packages I want to choose fromheading, add the line hr.irb.fastRandomForestDo not forget to add a comma and a backslash to the previous line. 5. Use the “FastRandomForest” class is in the hr.irb.fastRandomForest package in the "Classify" tab. The other three classes cannot be used directly. Using from Weka Explorer or Experimenter (versions 3.5.8 or 3.6.0 only)1. Add the FastRandomForest.jar to your Java classpath when starting Weka. This is normally done by editing the line beginning with “cp=” in “RunWeka.ini” 2. Extract the “GenericObjectEditor.props” file from weka.jar (jar files are in fact ordinary zip archives, the GenericObjectEditor.props is under /weka/gui). 3. Place the file you've just extracted into the directory where you have installed Weka (on Windows this is commonly "C:\Program Files\Weka-3-5") 4. Find the # Lists the Classifiers I want to choose fromheading and scroll far down to the end of the block (first empty line), then add a line: hr.irb.fastRandomForest.FastRandomForestDo not forget to append a comma and a backslash to the previous line. 5. The “FastRandomForest” class is in the "hr.irb.fastRandomForest" package in the "Classify" tab. Enjoy. [Less]

1.78K lines of code

0 current contributors

about 3 years since last commit

2 users on Open Hub

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Mostly written in Java

Licenses: GPL-2.0+

Tags classification classifier java randomforest weka

GNAT NER

Analyzed 11 months ago

GNAT is a library and web service capable of performing gene entity NER and normalization of biomedical articles. Mentions of genes and proteins in the articles are linked to to Entrez Gene identifiers. GNAT is available both for local download (suitable for large-scale processing) and as a web ... [More]

29.8K lines of code

1 current contributors

almost 2 years since last commit

2 users on Open Hub

Activity Not Available

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Mostly written in Java

Licenses: No declared licenses

Tags academic article articles bioinformatics classification contextualprofiles database entrez eutils gazeteers gazetteer gene 17 more...

OTB Applications (deprecated)

Analyzed over 1 year ago

OTB Applications are now included in the ORFEO ToolBox repository (see https://www.openhub.net/p/otb) This package includes a set of applications for remote sensing image processing such as orthorectification, classification, object extraction... Few of these applications come with a full GUI ... [More]

40.7K lines of code

0 current contributors

over 4 years since last commit

2 users on Open Hub

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Mostly written in C++

Licenses: No declared licenses

Tags application c++ classification gui image image_analysis imageprocessing image_processing imaging informationextraction mapping optical 8 more...

Multi-label Extension to Weka

Analyzed about 11 hours ago

Multi-label methods using the Weka and MOA machine learning frameworks

49.2K lines of code

5 current contributors

25 days since last commit

2 users on Open Hub

Moderate Activity

0 Reviews

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Mostly written in Java

Licenses: GPL-3.0+

Tags classification machine_learning mulan multilabel multitarget weka

mulan

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Analyzed about 1 year ago

We developed a package of Java classes for Multi-label classification, called Mulan. The package contain source files and compiled classes of several problem transformation methods for multilabel classification, an evaluation framework that computes several multilabel classification evaluation ... [More]

19.9K lines of code

0 current contributors

over 3 years since last commit

1 users on Open Hub

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Mostly written in Java

Licenses: GPL-2.0+

Tags algorithm classification classifiers machine_learning multilabel tagging

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