25 Java machine learning tools and libraries

25 Java machine learning tools and libraries

1. Weka integrates Machine Learning Algorithms for data mining. These algorithms can be directly applied to a dataset or you can write code to call them. Weka includes a series of tools, such as data preprocessing, classification, regression, clustering, association rules, and visualization.

2. Massive Online AnalysisMOA) is a popular open-source framework for data stream mining and has a very active growth community. It includes a series of machine learning algorithm classification, regression, clustering, exception detection, concept drift detection and Recommendation Systems) and evaluation tools. Associated with the WEKA project, MOA is also written in Java and has higher scalability.

3. The MEKA project provides an open-source implementation for multi-label learning and evaluation methods. In multi-label classification, We need to predict multiple output variables for each input instance. This is different from the "normal" case where only one single target variable is involved. In addition, MEKA's WEKA-based machine learning toolkit.

4. Advanced Data mining And Machine learning SystemADAMS) is a new type of flexible workflow engine designed to quickly establish And maintain a complex knowledge stream in the real world. It is released based on GPLv3.

5. Environment for Developing KDD-Applications Supported by Index-StructureELKI) is a Java-based open-source AGPLv3 data mining software. ELKI is mainly focused on algorithm research, focusing on unsupervised methods and exception detection in cluster analysis.

6. Mallet is a Java-based machine learning toolkit for text files. Mallet supports classification algorithms, such as maximum entropy, Naive Bayes, and decision tree classification.

7. encog is an advanced machine learning framework that integrates Support Vector Machine (SVM), artificial neural networks, genetic algorithms, Bayesian Networks, Hidden Markov Model (HMM), Genetic Programming, and genetic algorithms.

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

9. Deeplearning4j is the first commercial, open-source, and distributed Deep Learning Library written in Java and Scala. It is designed for commercial environments 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 Germany's dortmont University of Technology. It provides a GUI for developers to develop applications) and Java APIs. It also provides some machine learning algorithms for data processing, visualization, and modeling.

12. apache SAMOA is a machine learning ML framework. It is embedded with the programming abstraction for Distributed stream ML algorithms and allows DSPEe, for example, Apache Storm, Apache S4, and Apache samza. You can develop distributed stream ML algorithms and execute them on multiple DSPEs.

13. Neuroph simplifies neural network development by providing Java Network Libraries and GUI tools that support creating, training, and saving neural networks.

14. Oryx 2 is a Lambda architecture built on Apache Spark and Apache Kafka. However, with real-time large-scale machine learning, it is becoming more specialized. This is a framework for building applications, but it also includes packaging and end-to-end applications for collaborative filtering, classification, regression, and clustering.

15. Stanford Classifier is a machine learning tool that can classify data items. A probability classifier, for example, can give the probability distribution of class distribution for a data item. This software is a Java implementation of the Maximum Entropy classifier.

16. io is a Retina API with fast and accurate natural language processing algorithms similar to the brain.

17. JSAT is a quick start Machine Learning Library. This library was developed in my spare time and released based on GPL3. Some content in the library can be learned independently. For example, all codes are independent. JSAT does not have external dependencies and is written in pure Java.

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

19. Java Machine Learning LibraryJava Machine Learning Library) is the implementation of a series of Machine Learning algorithms. These algorithms are well written in both source code and documentation. The main language is Java.

20. Java-ML is a Java API that uses a series of machine learning algorithms written in Java. It provides only one standard algorithm interface.

21. MLlib (Spark) is an extensible Machine Learning Library of Apache Spark. Although it is Java, the library and platform also support binding Java, Scala and Python. This library is up-to-date and has many algorithms.

22. H2O is a machine learning API for smart applications. It scales statistics, machine learning, and mathematics on big data. H2O is scalable. developers can use simple mathematical knowledge in the core part.

23. WalnutiQ is an object-oriented model of the human brain. Some Learning Algorithms commonly used in theory are being studied in the direction of simple and strong AI models ).

24. RankLib is a ranking Learning Algorithm Library. Currently, eight popular algorithms have been implemented.

25. htm. java is implemented based on the Hierarchical Temporal Memory algorithm of Java. It is a Java interface for the intelligent computing Numenta platform. Source code