Search Results for "machine learning python" - Page 4

The aim of the BioWeka project is to add bioinformatics functionalities such as e.g. alignments to the popular machine learning framework Weka.

Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machine learning problems and a specific set of models for gene prediction using semi-Markov CRFs.

pyPal is a jabber based chatterbot that can be used to launch commands remotely as well as to make some good conversation. It is expected to be capable of multi-language learning.

pynpvm is a Python interface to Parallel Virtual Machine (PVM), a portable heterogeneous message-passing system. It requires NumPy as a pre-requisite because the datatype numpy.array is used for message passing.

Cellogica is a cellular logic analysis tool. It incorporates sequential logic and finite state machine to elucidate the relationship between transcription factors and corresponding gene expression.

MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machine learning. The code is well documented and easy to extend, especially for adding new weak learners.

General purpose agents using reinforcement learning. Combines radial basis functions, temporal difference learning, planning, uncertainty estimations, and curiosity. Intended to be an out-of-the-box solution for roboticists and game developers.

The Wolfram Machine project is an effort to create a set of documentation and useful modules (both hardware and software) for a computing architecture based on the mathematical theories presented in Steven Wolfram's book _A_New_Kind_of_Science_.

Software to fit whole-sentence language models using the principle of maximum entropy. For developers of speech recognizers, text prediction interfaces, OCR, machine translation software.

This lets you used the PVM (parallel virtual machine) API from within Python.

The TreeQ package is a set of C-language applications that implement a automatic machine learning algorithm based on a tree-structured classifier. This approach is particularly effective for high-dimensional continuous data such as audio and video.

JUDGE (Java Utility for Document Genre Eduction) features automatic classification and clustering of documents, optionally as a webservice. The program is written entirely in Java and makes use of the Weka machine learning toolkit.

HORUS is a system for knowledge acquisition, hypothesis generation, inference and learning. It is an interactive, internet environment accessible to a diverse community of users (public-access or membership basis) - see also UMKAILASH project for more.

Weka-Parallel is a modification to Weka, created with the intention of being able to harness the power of Weka and the speed of parallel processing to be able to run a number of data mining and machine learning algorithms quickly.

Emily is a friendly name for the Machine Learning Environment (MLE). This project is at an early stage of development, and no alpha code is yet available.

ITAS puts a human user in the place of a Robocup agent by providing a visual display of the agent's environment and an interface for controlling the agent. It also generates a log of the user's environment and actions for use in machine learning.

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.

The ROSETTA C++ library is a collection of C++ classes and routines that enable discernibility-based empirical modelling and data mining. Comprises useful routines for machine learning in general and for rough set theory in particular.

Using Machine Learning, AraRooter finds the three-lettered root of any Arabic lemma with around 84% accuracy.

a distributed engine for abstract neural network development via natural-language programming

Cinefile is a prototype of a category-based method of database exploration. It allows the user to identify abstract categories of films by providing examples of category members, learns to classify films as belonging or not belonging to those categories, and provides a graphical interface for exploring and comparing categories. Cinefile is designed to work with data retrieved from the Internet Movie Database (imdb.com). This data is used for classification and is the subject of the...

Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree.

R based genetic algorithm for optimization, variable selection and other machine learning and statistical analysis approaches.

ScenConnect shows scenarios as networks of situation and event tag sets, for fast comparisons. It links scenarios to tags, scores, and other metadata, creating situationals suitable for search, mining, machine learning, and planning.