Projects tagged ‘classifier’

NLTK — the Natural Language Toolkit — is a suite of open source Python modules, linguistic data and documentation for research and development in natural language processing, supporting dozens of ... [More] NLP tasks, with distributions for Windows, Mac OSX and Linux. [Less]

A high-level interface to the CMU Link Grammar. This binding wraps the link-grammar shared library provided by the AbiWord project for their grammar-checker.

TestEl is a Java-based learning analyzer for HTML (and possibly other) structured documents. It can be trained to detect structures in such documents and renders hits in XML.

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. ... [More] Speed gains depend on many factors, but a 5-10x increase over Weka 3-6-1 on a quad core computer 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]

Ruby interface to the CRM114 Controllable Regex Mutilator, an advanced and fast text classifier that uses sparse binary polynomial matching with a Bayesian Chain Rule evaluator and a hidden Markov ... [More] model to categorize data with up to a 99.87% accuracy. [Less]

# Project Name: nbc # Authiors : T.S.Yo and S. Sinha # Last Updated: 2005.11.11 # Description:   An implementation of Naive Bayes Classifier in S-language, tested under S-PLUS. # ... [More] Correction 2005.11.11: In nb.train() we privously use Pr(Ai,C) instead of Pr(Ai|C), this problem is fixed in this version. # ./src/ File Name Description ----------------- ---------------------------------------------------- NB_functions.ssc A S-Plus™ Script file contains the developed                     functions, nb.train and nb.ptrdict. Executingthis                     script will install these two functions as objects.                      RunTest.ssc A S-Plus™ Script file to read in the WSBC data set                     and perform a single test. Executing this script                     will create a data frame bcdata, a naive Bayes model                     nb using the first 400 records, and a prediction                     object pred using the later 283 records. bc.data The WSBC data set. The values “?” in the original                     database are replaced by NA (missing). RunBootstrap.ssc A S-Plus™ Script file to read in the WSBC data set                     and perform a L=100 bootstrapping experiment.                     (This script will run for a very long time!) RunCV.ssc A S-Plus™ Script file to perform a 5-fold cross                     -validation experiment. Executing this script will                     create a list object cvcfm contains the confusion                     matrix for each fold, and an array cverr to hold the                     five error rates. ----------------- ----------------------------------------------------   # Procedure to install and test the developed functions     First, unzip the files in appendixA.zip into the working directory.     For installation, execute the NB_functions.ssc.     After installation, execute the RunTest.ssc to test the functions.     After the installation of the function, both RunBootstrap.ssc and     RunCV.ssc can be executed for further testing. [Less]

C4.5 is a well-known machine learning algorithm used widely, but its runtime performance is sacrificed for the consideration of the limited main memory at that time. We present a fast implementation ... [More] of C4.5 algorithm, named FC4.5(Fast C4.5). It organizes novel data structures, uses the indirect bucket-sort combined with the bit-parallel technique, and confines the binary-search of the cutoff within the narrowest range. The combination of these techniques enables FC4.5 greatly accelerate the tree construction process of C4.5 algorithm. Experiments show that FC4.5 can build the same decision tree as C4.5(Release 8) system with a runtime performance gain up to 5.8 times. Besides, FC4.5 also achieves a good scalability on different kinds of datasets. [Less]

M3 Frame

Data repository for clock gene classifier

This code uses a k nearest neighbor classifier to utilize domain knowledge form past experiences. Its goal is to prove that adaptive AI can be applied realistically in a Real Time Strategy game and ... [More] that gathered knowledge can be efficiently transfered in different maps. This is actually kind of a repetition of the work of Bakkes and Spronck (see on the side). [Less]