17 projects tagged "machine learning"
Updated 17 Mar 2013
SHOGUN
| Pop | 295.45 |
|---|---|
| Vit | 31.20 |
SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.
Updated 13 May 2013
Armadillo C++ Library
| Pop | 439.57 |
|---|---|
| Vit | 98.94 |
Armadillo is a C++ linear algebra library (matrix maths) aiming towards a good balance between speed and ease of use. Integer, floating point, and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. A delayed evaluation approach, based on template meta-programming, is used (during compile time) to combine several operations into one and reduce or eliminate the need for temporaries.
Updated 04 Jul 2011
K-tree
| Pop | 81.89 |
|---|---|
| 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 22 May 2013
Thinknowlogy
| Pop | 189.62 |
|---|---|
| Vit | 93.78 |
Thinknowlogy is grammar-based software designed to utilize the logic contained within grammar in order to create intelligence through a natural language, which is demonstrated by programming in a natural language, reasoning in a natural language (drawing conclusions, making assumptions (with a self-adjusting level of uncertainty), asking questions (about gaps in the knowledge), and detecting conflicts), and intelligent answering of "is" questions, providing alternative answers as well.
Updated 19 Aug 2009
x2search
| Pop | 21.47 |
|---|---|
| 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 | 63.87 |
|---|---|
| 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 | 41.67 |
|---|---|
| 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 06 May 2010
scikits.learn
| Pop | 22.41 |
|---|---|
| Vit | 33.39 |
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 | 147.44 |
|---|---|
| Vit | 12.47 |
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 | 83.90 |
|---|---|
| Vit | 4.87 |
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.
