Java machine learning tools & Libraries, java machine learning tools

Java machine learning tools & Libraries, java machine learning tools

The following is a list of 25 Java machine learning tools & Libraries:

1. Weka is a collection of machine learning algorithms for data mining tasks. These algorithms can be directly applied to datasets or called in your own Java code. Weka includes tools such as data preprocessing, classification, regression, clustering, association rules, and visualization.

2. Massive Online Analysis (MOA) is a very popular open-source framework for data mining. It has a very active community. It includes a set of machine learning algorithms (classification, regression, clustering, exception detection, concept drift detection and Recommendation Systems) and evaluation tools. Like the WEKA project, MOA is also written in Java, but it is more scalable.

3. The MEKA project provides an open-source implementation of multi-tag learning and evaluation methods. In multi-tag classification, We need to predict multiple output variables for each input instance. This is different from the "standard" that only involves a single target variable. MEKA is based on WEKA machine learning toolkit.

4. Advanced Data mining And Machine learning System (ADAMS) is a new And flexible working engine designed to quickly build And maintain the real world, complex knowledge processes, And release based on GPLv3.

5. Environment for Developing KDD-Applications Supported by Index-Structure (ELKI) is an open-source (AGPLv3) data mining software written in Java. ELKI focuses on the unsupervised methods in cluster analysis and outlier detection algorithm.

6. Mallet is a machine learning toolkit for Java text documents. Mallet supports the maximum entropy, naive bayes, and decision tree classification algorithms.

7. Encog is an advanced machine learning framework that supports Support Vector Machines, artificial neural networks, genetic programming, Bayesian Networks, Hidden Markov Models, genetic programming, and genetic algorithms.

8. Datumbox is an open-source machine learning framework written in Java that allows you to quickly develop machine learning and statistical applications. The main focus of this framework is to include a large number of machine learning algorithms and statistical tests, and to be able to process medium-scale datasets.

9. Deeplearning4j is the first commercial, open-source, and distributed Deep Learning Library written in Java and Scala. It is intended to be used in a business environment, rather than as a research tool.

10. Mahout is a machine learning framework with built-in algorithms. Mahout-Samsara helps people create their own mathematics and provides some ready-made algorithm implementations.

11. Rapid Miner was developed by the University of Dortmund in Germany. It provides a graphical user interface and Java API for users to create their own applications. It provides data processing, visualization, and machine learning algorithm modeling.

12. Apache SAMOA is a machine learning framework. It contains a distributed streaming media programming abstract ML algorithm, so that developing new ML Algorithms does not need to directly process complex underlying distributed stream Processing engines (DSPEe, such as Apache Storm, Apache S4, and Apache Samza ). Its users can develop distributed streaming media ML algorithms at a time and execute multiple DSPEs.

13. Neuroph simplifies the development of neural networks by providing Java neural networks that support the creation, training, and preservation of neural networks.

14. Oryx 2 is a lambda architecture built on Apache Spark and Apache Kafka, but specialized in real-time large-scale machine learning. It is a framework for creating applications, but it also provides packages, as well as terminals to terminals for collaborative filtering, classification, regression, and clustering.

15. Stanford Classifier is a machine learning tool that obtains data and divides them into K classes. This software is a Java-implemented Maximum Entropy classifier.

16. Cortical. io is a fast, accurate, and brain-like Retina API.

17. JSAT is a quick start library for machine learning. It was developed in my spare time and can be used in GPL 3. Part of the library is self-education, so all code is self-contained. JSAT is pure Java and has no external dependencies.

18. N-Dimensional Arrays for Java (ND4J) is a scientific computing library of JVM. They are used in the production environment, which means that the program is designed to run with minimal memory requirements.

19. Java Machine Learning Library is a reference implementation for a group of Machine Learning algorithms. These algorithms are recorded, including the source code, and are recorded on the document website. It is mainly written in Java.

20. Java-ML is a Java API and a collection of machine learning algorithms implemented by Java. It provides only one standard algorithm interface.

21. MLlib (Spark) is an Apache Spark extended Machine Learning Library. Although it is Java, the Library provides Java, Scala, and Python bindings. The library is new, and the list of algorithms is very long.

22. H2O is a machine learning API for smart applications. It extends statistics, machine learning, and big data operations. H2O is extensible.

23. WalnutiQ is an object-oriented model that theoretically has a common learning algorithm with some human brains (the goal of work is a simple AI model with emotion ).

24. RankLib is a Sort Learning Algorithm Library. Eight popular algorithms have been implemented.

25.htm. java (Hierarchical Temporal Memory implementation in Java) is a Java port of the intelligent learning platform Numenta.

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