Machine learning algorithms for advanced analytics
OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. Some typical applications of OpenNN are business intelligence (customer segmentation, churn prevention…), health care (early diagnosis, microarray analysis…) and engineering (performance optimization, predictive maitenance…). OpenNN does not deal with computer vision or natural language processing. The main advantage of OpenNN is its high performance. This library outstands in terms of execution speed and memory allocation. It is constantly optimized and parallelized in order to maximize its efficiency. The documentation is composed by tutorials and examples to offer a complete overview about the library. OpenNN is developed by Artelnics, a company specialized in artificial intelligence.
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is available both for Unix and Windows platforms (a dedicated platform archive is available on request). Note : if you have downloaded version 3.12 before the 8th of february, a patch exists for a minor bug on TOutputFileKey file, don't hesitate to ask us.
UI for fscaret
User Interface (ui) application which implements the automated feature selection provided by the 'fscaret' package of R-environment.
Dynamic Generalized Relevance Learning Vector Quantization
Some of the usual problems for Learning vector quantization (LVQ) based methods are that one cannot optimally guess about the number of prototypes required for initialization for multimodal data structures i.e.these algorithms are very sensitive to initialization of prototypes and one has to pre define the optimal number of prototypes before running the algorithm. If a prototype, for some reasons, is ‘outside’ the cluster which it should represent and if there are points of a different categories in between, then the other points act as a barrier and the prototype will not find its optimum position during training. Since the model complexity is not known in many cases, we avoid this problem by introducing a "Dynamic" version of LVQ. Dynamic-GRLVQ (DGRLVQ), which adapts the model complexity to the given problem during training by adding or removing prototypes dynamically/realtime one by one for each category until satisfactory classification results are achieved.
EpochX is an open source genetic programming framework, specifically for analysing the properties of evolutionary automatic programming. It supports 3 popular representations - Strongly-Typed GP, Context-Free Grammar GP and Grammatical Evolution.
This project develops a simple, fast and easy to use Python graph library using NumPy, Scipy and PySparse.
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.
BCAR is a library for the associative classification, which denotes "Boosting Class Association Rules". BCAR provides a general tool for classification tasks with various types of input data.
An open source optical flow algorithm framework for scientists and engineers alike.
CoveringToolKit is a software toolkit implement the Covering Algorithm in Machine Learning Field.
ECOC PAK is a C++ Library for the Error Correcting Output Codes classification framework. It supports several coding and decoding strategies as well as several classifiers.
A graphical MatLab framework for estimating the parameters of, modeling and simulating static and dynamic linear and polynomial systems in the errors-in-variables context with the intent of comparing various estimation strategies.
Generator for optimized, vectorized neural net code
This Ruby program takes in a topology specification for an artificial neural network and emits optimized C code (using SSE intrinsics) that implements fast forward and backward propagation for that specific topology.
neural network implementation in java
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"
A collection of algorithms based on the topology preserving Neural Gas algorithm for density estimation/quantization/clustering/self-organized learning. I moved this project to GitHub: https://github.com/sergioroa/neuralgas
This project aims to develop a method to identify communities in a social network according to some point of view.
The Python Computer Vision Framework is an opened project deisgned for all those interested in computer vision. It aims at making computer vision more easy and structured and matlab-free. It may also be used for other artistic and scientific areas.
This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system. Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
Text annotation application (Tapp) is a stand alone software component that facilitates the quick annotation of text files for the purpose of creating labelled data for training, testing, and deploying machine learning models
Java application for training and deploying text processing applications such as part-of-speech taggers, based on a re-implementation of Brill's algorithm in Java.
Acquire and manipulate images from a camera in real-time
An application that allows you to acquire images from a camera and process this image in real-time. This should enable tracking, classifying, measuring and masking objects and areas of interest in an image. This application should in the future allow monitoring areas and triggering actions based on certain events.
Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.
Activity-Miner for Android
A mobile application to create accelerometer based activity recognition models directly on the phone. The configuration of the segmentation and feature extraction process chain requires expert knownledge. The prototype was developed in 2012 in a bachelor thesis at the University of Kassel and was optimized and enhanced for an experiment in 2015.
Feature Graphs Miner
An information extraction library implementing modern algorithms for the extraction of named entities from text.