Open Machine Learning will be a collection of data structures and algorithms written in C++ that enables machine learning research.
Simple .m files, Basic Neural Networks study for Octave (or Matlab)
--> For a more detailed description check the README text under the 'Files' menu option :) The project consists of a few very simple .m files for a Basic Neural Networks study under Octave (or Matlab). The idea is to provide a context for beginners that will allow to develop neural networks, while at the same time get to see and feel the behavior of a basic neural networks' functioning. The code is completely open to be modified and may suit several scenarios. The code commenting is verbose, and variables and functions do respect English formatting, so that code may be self explanatory. Messages to the screen are localized, both in English and Spanish, and it is really easy to add another language to the localization. If any further explanation is needed, the forum/discussion page may be of help :) Comments and suggestions are very welcome.
DE-based Weight Optimisation for Heterogeneous Ensemble
We propose the use of Differential Evolution algorithm for the weight adjustment of base classifiers used in weighted voting heterogeneous ensemble of classifier. Average Matthews Correlation Coefficient (MCC) score, calculated over 10-fold cross-validation, has been used as the measure of quality of an ensemble. DE/rand/1/bin algorithm has been utilised to maximize the average MCC score calculated using 10-fold cross-validation on training dataset. The voting weights of base classifiers are optimized for the heterogeneous ensemble of classifiers aiming to attain better generalization performances on testing datasets.
An implementation of a new proposed model of smoothly spiking neural networks + a fully analytical gradient descent algorithm.
Obstacle Avoidance in Player/Stage using the ProBT library.
Medical Datasets (In a text file, with space separated values) can be loaded to the system. By choosing either one of the two classifiers, Neural network or Decision Tree, the system can be trained and evaluated.
MultiNest is a Bayesian inference tool for efficient Bayesian analysis of highly complex probability distributions.
data of robot.
data of my robot or other software.
RNG stands for Robocode Next Generation. RNG is to be the next reference for the most advanced and innovative Robocode programming techniques.
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"
This is an Internet resource management system that can be used to control both internet and network access of a registered or unregistered user on all kinds of network.
Differentially-private algorithm based on Generalization
Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, epsilon-differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary's background knowledge. All the existing solutions that ensure epsilon-differential privacy handle the problem of disclosing relational and set-valued data in a privacy preserving manner separately. We developed an algorithm that considers both relational and set-valued data in differentially private disclosure of healthcare data.
Machine Learning framework in Python
Extended Supervised Tracking and Classifying System
This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ExSTraCS was primarily developed to address problems in epidemiological data mining to identify complex patterns relating predictive attributes in noisy datasets to disease phenotypes of interest. ExSTraCS combines a number of recent advancements into a single algorithmic platform. It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7.
Fast EXperimentation with Neural Networks
FENNIX is a simulator of artificial neural networks written in Java. It allows you to easily describe a complete simulation by using a simple text script language or by adding nodes to a tree of tasks by using the graphical used interface. Moreover, FENNIX is composed of pluggable tools that can be easily modified in order to add new functionalities to the simulator.
A vocabulary tree for image classification using OpenCV
A vocabulary tree for image classification have been designed to be integrated in mobile robotic applications. It is a learning schema based on decission trees, bags of features and inverted files. The design provides training and optimization parameters that have been characterized using several detectors and descriptors for several input datasets. Evaluation tests performed on public image databases allow to compare obtained results with previously published literature. All the tools and resources used in this project are Open Source licensed.
Incremental and local outlier detection
Compares botnet detection methods
Compares botnet detection methods by computing the error metrics by reading the labels on a NetFlow file. The original NetFlow should have a new column for the ground-truth label, and a new column with the prediction label for each botnet detection method. This program computes all the error metrics (TPR, TNR, FPR, FNR, Precision, Accuracy, ErrorRate, FMeasure1, FMeasure2, FMeasure0.5) and output the comparison results. It also ouputs a png plot. The program can compare in a flow-by-flow basis, or it can apply our new botnet detection error metrics, that is time-based, detects IP addresses instead of flows and it is weighted to favor sooner detections. See the paper for more details.
Library written in C with Python API for IPv6 networking
This project is a rewritten of an initial project that I've called GLUE and created in 2005. I'm trying to readapt it for Python 2.7.3 and GCC 4.6.3 The library has to be build as a simple Python extension using >python setup.py install and allows to create different kind of servers, clients or hybryds (clients-servers) over (TCP/UDP) using the Ipv6 Protocol. The architecture of the code is based on neuron architecture. Will put an IPv6 adress active (one or more on my wireless network card) as soon as possible available so that you can download codes.
Bioinformatics Artificial Intelligence Order
A smart interface of AI that will interrogate and complete your bioinformatics data analysis for you. Download and start your instance of BAIO to join the network of great bioinformatics Minds.
Neuroph OCR - Handwriting Recognition is developed to recognize hand written letter and characters. It's engine derived's from the Java Neural Network Framework - Neuroph and as such it can be used as a standalone project or a Neuroph plug in.
The Janelia Automated Animal Behavior Annotator
The Janelia Automatic Animal Behavior Annotator (JAABA) is a machine learning-based system that enables researchers to automatically compute interpretable, quantitative statistics describing video of behaving animals. Through our system, users encode their intuition about the structure of behavior by labeling the behavior of the animal, e.g. walking, grooming, or following, in a small set of video frames. JAABA uses machine learning techniques to convert these manual labels into behavior detectors that can then be used to automatically classify the behaviors of animals in large data sets with high throughput. JAABA combines an intuitive graphical user interface, a fast and powerful machine learning algorithm, and visualizations of the classifier into an interactive, usable system for creating automatic behavior detectors. Documentation is available at: http://jaaba.sourceforge.net/