14 projects tagged "machine learning"
Updated 04 Jul 2011
K-tree
| Pop | 82.83 |
|---|---|
| Vit | 2.17 |
K-tree provides a scalable approach to clustering by combining the B+-tree and k-means algorithms. Clustering can be used to solve problems in signal processing, machine learning, and other contexts. It has recently been used to solve document clustering problems on the Wikipedia collection.
Updated 19 Aug 2009
x2search
| Pop | 21.66 |
|---|---|
| Vit | 1.00 |
x2search is a crawler based on machine learning algorithms that finds pages and documents that are similar to given positive and different to given negative examples. The learned classifiers can be exported and saved for later reuse. It features multiple settings for searching by domain/server, etc. and has a plug-in mechanism for adding document types to be searched.
Updated 01 Oct 2010
Pinta
| Pop | 63.80 |
|---|---|
| Vit | 2.63 |
Pinta is an extremely versatile, extensible, self-learning image classification program. It uses texture and color analysis and neural network techniques to automatically learn differences in images. It comes with a C API for easy integration into other software. It is built on top of the pattern recognition and image analysis platform Into.
Updated 13 Aug 2010
TexLexAn
| Pop | 41.52 |
|---|---|
| Vit | 1.11 |
TexLexAn allows you to drag and drop a document or a link to any Web page to analyze. The program will analyze the text, evaluate its readability, categorize the document, summarize it, and archive it.
Updated 28 Jan 2010
Waffles
| Pop | 30.95 |
|---|---|
| Vit | 34.79 |
Waffles is a cross-platform C++ library of algorithms for machine learning, artificial intelligence, data mining, etc. It also contains demo apps and command-line wrapper tools that are useful for visualizing, analyzing, and predictively modeling data.
Updated 06 May 2010
scikits.learn
| Pop | 22.67 |
|---|---|
| Vit | 33.36 |
scikits.learn is a Python module that integrates classic machine learning algorithms in the tightly-knit world of scientific Python packages. It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.
Updated 17 Jan 2013
Milk
| Pop | 149.00 |
|---|---|
| Vit | 12.56 |
Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, and decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems. For unsupervised learning, milk supports k-means clustering and affinity propagation.
Updated 06 Nov 2012
Accord.NET Framework
| Pop | 84.10 |
|---|---|
| Vit | 4.89 |
Accord.NET provides statistical analysis, machine learning, image processing, and computer vision methods for .NET applications. The Accord.NET Framework extends the popular AForge.NET with new features, adding to a more complete environment for scientific computing in .NET.
Updated 30 Dec 2012
MyMediaLite
| Pop | 85.90 |
|---|---|
| Vit | 12.56 |
MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms. It addresses the two most common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars), and item prediction from implicit feedback (e.g. from clicks or purchase actions). It contains dozens of recommender engines, including state-of-the-art matrix factorization methods. It also supports real-time updates to the recommender engines, storing engines to disk and reloading them again, and several evaluation measures to compare the accuracy of different recommender system methods. Three command-line programs that offer most of the functionality contained in the library are included.
Updated 22 Aug 2011
Mee5
| Pop | 26.80 |
|---|---|
| Vit | 1.01 |
Mee5 is a set of runtime bindings for the See5 decision tree. It provides a shared library (or DLL) and C, C++, and Python bindings, and adds the possibility to have many clearly separated instances of an application's trees.
