Deploy in 115+ regions with the modern database for every enterprise.
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
Start Free
$300 Free Credits to Build on Google Cloud
New customers can spin up VMs, build with AI, and query data at no cost.
Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
Structlab is a machinelearning C++ framework for structured domains, which provides a toolbox of learning methods and tools for preprocessing and visualization. It also provides a GUI to setup elaborate experiments in a visual and intuitive way.
Zabal6 is a machinelearning student tool based on decision tree learning, focused in the area of knowledge discovery (data mining), and inspired on See5. Zabl6 is a C++ program for Linux and windows O.S, with a intuitive graphical interface.
Machinelearning toolkit for unsupervised and semi-supervised clustering that demonstrates excellent results on real-world data (see Bekkerman et al. ICML-2005 and ECML-2006).
PCP (Pattern Classification Program) is an open-source machinelearning program for supervised classification of patterns. PCP is a binary executable running on Linux and Windows (under Cygwin environment).
MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machinelearning. 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 Pattern Analysis Library (PALib) is a C++ class library for pattern classification/recognition. PALib consists of a wide range of machinelearning routines such as Bayesian decision theory, artificial neural networks, and fuzzy inference systems.
FLPD is an automatic learning system based on fuzzy prototypes, composed of a C++ library for machinelearning and fuzzy logic and an experimentation framework.
Emily is a friendly name for the MachineLearning Environment (MLE). This project is at an early stage of development, and no alpha code is yet available.
Secure by design, portable by default. Recover clean, fast, anywhere. Start a free trial.
Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
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 machinelearning in general and for rough set theory in particular.
Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other
Weka++ is a collection of machinelearning and data mining algorithm implementations ported from Weka (http://www.cs.waikato.ac.nz/ml/weka/) from Java to C++, with enhancements for usability as embedded components.