Simple Project List 軟體列表

63 projects in result set
最後更新: 2019-09-23 04:03

dlib C++ Library

ネットワーク、スレッド(メッセージパッシング、futures, 他)、グラフィカルインターフェイス、データ構造、線形代数、機械学習、XMLとテキスト解析、数値最適化、ベイズネット等を扱う移植可能なアプリケーションを開発するためのライブラリ。

最後更新: 2014-06-02 01:05

Armadillo C++ Library

Armadillo is a C++ linear algebra library (matrix maths) aiming towards a good balance between speed and ease of use. The API is deliberately similar to Matlab's. 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 numerics 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.

最後更新: 2019-09-23 08:04

Weka---Machine Learning Software in Java

Wekaは、実世界でのデータマイニングの問題を解決するための機械学習(Machine Learning)アルゴリズムのコレクションです。これはJavaで書かれており、ほぼすべてのプラットフォーム上で動作します。アルゴリズムは、データセットに直接適用するか、自身のJavaコードから呼び出すか、どちらも可能です。

最後更新: 2014-06-03 01:58

Thinknowlogy

Thinknowlogy is grammar-based software, designed to utilize the Natural Laws of Intelligence in grammar, in order to create intelligence through natural language in software. This is demonstrated by programming in natural language, reasoning in natural language and drawing conclusions (more advanced than scientific solutions), making assumptions (with self-adjusting level of uncertainty), asking questions (about gaps in the knowledge), and detecting conflicts in the knowledge. It builds semantics autonomously (with no vocabularies or words lists), detecting some cases of semantic ambiguity. It is multi-grammar, proving that Natural Laws of Intelligence are universal.

最後更新: 2015-11-06 06:47

Scikit Learn

Pythonの機械学習フレームワーク

最後更新: 2012-02-14 19:35

weka neural network algorithms

このプロジェクトには、自己組織化マップ (SOM) など学習ベクトル量子化 (LVQ) ニューラル ネットワーク アルゴリズムの実装の weka のパッケージが含まれます。Weka の詳細については、http://www.cs.waikato.ac.nz/~ml/weka/を参照してください。

(Machine Translation)
最後更新: 2018-06-16 01:15

GPLAB

GPLAB は、MATLAB のための遺伝的プログラミング ツールボックスです。

(Machine Translation)
最後更新: 2014-02-17 20:04

SHOGUN

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.

最後更新: 2014-12-07 04:46

Kaldi

音声認識研究のツールキット

最後更新: 2018-03-29 15:24

MEKA

多重分類器とその評価方法 Weka の機械学習のフレームワークを使用して。

(Machine Translation)
最後更新: 2014-01-07 23:16

MLPACK

MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.

(Machine Translation)
最後更新: 2012-11-06 10:42

Accord.NET Framework

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.

(Machine Translation)
最後更新: 2012-10-15 16:23

Fuzzy machine learning framework

Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.

最後更新: 2013-06-19 17:48

Milk

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.

(Machine Translation)
最後更新: 2013-11-23 23:51

RecDB

RecDB is a recommendation engine built entirely inside PostgreSQL 9.2. It allows application developers to build recommendation applications using a wide variety of built-in recommendation algorithms such as user-user collaborative filtering, item-item collaborative filtering, and singular value decomposition. Applications powered by RecDB can produce online and flexible personalized recommendations to end-users. It is easily used and configured and allows novice developers to define a variety of recommenders that fits their application's needs in few lines of SQL. It can seamlessly integrate recommendation functionality with traditional database operations.

(Machine Translation)