Projects tagged ‘machine_learning’

Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code.

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]

RapidMiner (formerly YALE) is the most comprehensive open-source software for intelligent data analysis, data mining, knowledge discovery, machine learning, predictive analytics, forecasting, and ... [More] analytics in business intelligence (BI). RapidMiner provides more than 400 data mining operators, a graphical user interface (GUI), an online tutorial with hands-on data mining applications, a comprehensive PDF tutorial, many visualization schemes for data sets and data mining results, many different learning and meta-learning schemes ranging from decision tree and rule learners to neural networks, SVMs, ensemble methods, etc. RapidMiner is implemented in Java and available under GPL (GNU General Public License) as well as under a developer license (OEM license) for closed-source developers [Less]

Mahout's goal is to build scalable, Apache licensed machine learning libraries. Initially, we are interested in building out the ten machine learning libraries detailed in ... [More] http://www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf using Hadoop. While these algorithms are our initial focus, we welcome contributions of other machine learning approaches. [Less]

This project is a modern C++ library with a focus on portability and program correctness. It strives to be easy to use right and hard to use wrong. Thus, it comes with extensive documentation and ... [More] thorough debugging modes. The library provides a platform abstraction layer for common tasks such as interfacing with network services, handling threads, or creating graphical user interfaces. Additionally, the library implements many useful algorithms such as data compression routines, linked lists, binary search trees, linear algebra and matrix utilities, machine learning algorithms, XML and text parsing, and many other general utilities. [Less]

Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection ... [More] , generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression, GPR. RFE, I-RELIEF), and bindings to external ML libraries (libsvm, shogun, R). While it is not limited to neuroimaging data (e.g. FMRI) it is eminently suited for such datasets. [Less]

Developed by the Knowledge Discovery Lab at UMass directed by Prof. Jensen, Proximity is tool for describing, exploring, manipulating, and modeling the interconnections of people, places, things, and ... [More] events. It has a high-performance engine for storing very large datasets (tens of M of nodes and links), a visual query language for complex searches, and a set of powerful machine learning modules that build human-readable explanations of how the nodes interact and influence each other. Proximity has modeled the Hollywood world, studied how to prevent securities fraud, helped analysts in the intelligence community, modeled protein interactions in cells, described the behaviour of P2P networks, predicted the success of NFL coaches, and improved anonymization techniques in social networks [Less]

Ellogon is a multi-lingual, cross-platform, general-purpose language engineering environment, developed in order to aid both researchers who are doing research in computational linguistics, as well as ... [More] companies who produce and deliver language engineering systems. Ellogon as a language engineering platform offers an extensive set of facilities, including tools for processing and visualising textual/HTML/XML data and associated linguistic information, support for lexical resources (like creating and embedding lexicons), tools for creating annotated corpora, accessing databases, comparing annotated data, or transforming linguistic information into vectors for use with various machine learning algorithms. [Less]

The Open Cognition Framework (OpenCog) is software for the collaborative development of safe and beneficial Artificial General Intelligence. OpenCog provides research scientists and software ... [More] developers with a common platform to build and share artificial intelligence programs. Programs written or adapted for OpenCog may be combined and used in concert with one another for experimentation or to achieve better results compared to their stand-alone counterparts. OpenCog is under active development, with the first release expected in 4Q-2008. [Less]

RL-Glue is both a set of ideas and standards, as well as a software implementation provided through this site. In theory, RL-Glue is a set of common guidelines for all RL researchers to follow to ... [More] allow researchers to share and compare agents and environments with greater ease. The software implementation of RL-Glue is the reusable glue to connect the basic parts of an experiment. RL-Glue is functionally a test harness to "plug in" agents and environments and experiment without having to continually rewrite the connecting code. As of RL-Glue 2.0, connecting agents and environments and experiments over a network is now supported. [Less]