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Conversion between different grammar frameworks is of great importance to comparative performance analysis of the parsers developed on them. This tool can convert CCG derivations to PTB trees by using Max Entropy models as well as visualizing the tree graphs. The main technical innovation presented here is the effective conversion method which achieves a F score over 95%.
This project contains weka packages of neural networks algorithms implementations like Learning Vector Quantizer (LVQ) and Self-organizing Maps (SOM). For more information about weka, please visit http://www.cs.waikato.ac.nz/~ml/weka/
This Java software implements Profile Hidden Markov Models (PHMMs) for protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.
3-layer neural network for regression and classification with sigmoid activation function and command line interface similar to LibSVM.
Quick Start: "java -jar nen.jar"
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...Its design has been applied to many natural language and other phenomena which exhibit variable behavior. A Perl XS implementation is available from http://humanities.byu.edu/am/ . This project is a Java implementation of the same. For more information on Analogical Modeling, see http://en.wikipedia.org/wiki/Analogical_modeling .
The objective of this project is to make available an open-source of a gridified version of the Multifactor Dimensionality Reduction (MDR) software (http://www.epistasis.org/software.html).
This is a RapidMiner extension replacing the current Weka-Plugin with the updated 3.7.3 Weka-Version. This is basically a branch of the 3.7.3 Version of WEKA wrapped into the old extension. New Features Include:
-All the Features of the 3.7.3 Weka Package
-Multi-Threaded ensemble learning
-An enhancement on the popular RandomForest Learner based on "Dynamic Integration with Random Forests" by Tsymbal et al. 2006 and "Improving Random Forests" by Robnik-Sikonja 2004.
-More enhancements...
“Neurology Diagnosis System” is a web-based expert system for diagnosis of neurologic disorders or the disorders of our nervous system. Health assistants in remote areas can use the system to diagnose neurologic patients in the absence of neurolo
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This Project is to make a robotic platform and Soft Brain for a self learning research robot.
For making it modular we are using OSGI with rosjava javacv.
Enable the user to talk to the calculator to enter the numbers and operations or write them using a drawing panel , it support both English and Arabic languages.It have text to speech of the entered numbers/operations and reading the results.
MultiViL is a tool for multi-view learning. It supports four classifiers (KNN, Naive-Bayes, Rochio and SVM-Perf), four view combining methods (Majority Voting, Borda Count, Dempster-Shafer theory of evidence and PSO) and provides many analisys tools.
Feating constructs a classification ensemble comprising a set of local models. It is effective at reducing the error of both stable and unstable learners, including SVM. For details see the paper at http://dx.doi.org/10.1007/s10994-010-5224-5.
RoboBeans is an interface to the "Robocup 2D Soccer Simulation Server" that allows developers to write Robocup teams\agents concentrating on behaviour and AI without having to worry about syntax of communication or network issues.
The aim of ALIVE is to develop new approaches to the engineering of flexible, adaptable distributed service-oriented systems based on the adaptation of social coordination and organisation mechanisms.
KeplerWeka adds the functionality of the open-source machine learning and data mining workbench WEKA to the free and open-source, scientific workflow application, Kepler.
BorderFlow implements a general-purpose graph clustering algorithm. It maximizes the inner to outer flow ratio from the border of each cluster to the rest of the graph.
ML@IUL is a class-project of the Machine Learning (ML) course at DCTI (Department of Computer Science and Technology), IUL (ISCTE - Lisbon University Institute). The objective is to create an ML library from student assignments.
SAIM allows to interlink knowledge bases in the Semantic Web. It focuses on instance matching of very large knowledge bases available as SPARQL endpoints. SAIM uses machine learning techniques and is compatible with SILK.