軟體列表

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.

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.

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.

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.

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.

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.

このプロジェクト (CvHMM) の離散隠れマルコフ モデル (OpenCV に基づく HMM) の実装です。それは理解し、やすく使いやすいです。Zip ファイルには実装の 1 つ main.cpp ファイルどのように作品のデモを 1 つのヘッダーが含まれています。あなたのプロジェクトに役になればと願っています。

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。