25 Java Machine Learning Tools and Libraries

25 Java Machine Learning Tools and Libraries
The IT industry is becoming more and more popular. As more new forces join the IT family, Java accounts for an increasing proportion. Here are some learning tools for you.
1. Weka integrates machine learning algorithms for data mining work. These algorithms can be applied directly to a data set or you can write your own code to call them. Weka includes a series of tools such as data preprocessing, classification, regression, clustering, association rules and visualization.
2.Massive Online Analysis (MOA) is a popular open source framework for data stream mining and has a very active growing community. It includes a range of machine learning algorithms (classification, regression, clustering, anomaly detection, concept drift detection, and recommendation systems) and evaluation tools. Associated with the WEKA project, MOA is also written in Java and is more scalable.
3. The MEKA project provides an open source implementation of multi-label learning and evaluation methods. In multi-label classification, we want to predict multiple output variables for each input instance. This is different from the "ordinary" case involving only a single target variable. In addition, MEKA is based on WEKA's machine learning toolkit.
4. Advanced Data mining And Machine learning System (ADAMS) is a new type of flexible workflow engine designed to quickly establish and maintain complex knowledge flows in the real world. It is released based on GPLv3.
5. Environment for Developing KDD-Applications Supported by Index-Structure (ELKI) is a Java-based open source (AGPLv3) data mining software. ELKI mainly focuses on algorithm research, focusing on unsupervised methods and anomaly 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 machines (SVM), artificial neural networks, genetic algorithms, Bayesian networks, hidden Markov models (HMM), genetic programming and genetic algorithms.
8. The Datumbox machine learning framework is an open source framework written in Java that allows the rapid development of machine learning and statistical applications. The core focus of the framework includes a wide range of machine learning algorithms as well as statistical tests, capable of handling medium-sized data sets.
9. Deeplearning4j is the first commercial-grade, open source, distributed deep learning library written in Java and Scala. It is designed for use in a business environment, not 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 Technical University of Dortmund in Germany. It provides a GUI (Graphical User Interface) and Java API for developers to develop applications. It also provides some machine learning algorithms for data processing, visualization and modeling.
12. Apache SAMOA is a machine learning (ML) framework that embeds programming abstractions for distributed stream ML algorithms, and allows for processing without directly dealing with the underlying distributed stream processing engine (DSPEe, such as Apache Storm, Apache S4 and Apache samza ) complexity, develop new ML algorithms. Users can develop distributed streaming ML algorithms that can be executed 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 implementation built on Apache Spark and Apache Kafka, but increasingly specialized for real-time large-scale machine learning. This is a framework for building applications, but 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 place data items into a category. A probabilistic classifier, such as this one, can give a probability distribution of class assignments over a data item. This software is a Java implementation of the maximum entropy classifier.
16.io is a Retina API with fast and accurate brain-like natural language processing algorithms.
17.JSAT is a quick-start machine learning library. This library was developed in my spare time and released based on GPL3. Part of the content in the library can be learned independently, for example, all codes are independent. JSAT has no external dependencies and is written in pure Java.
18. N-Dimensional Arrays for Java (ND4J) is a scientific computing library for JVM. They are intended for use in a production environment, which indicates that the routines are designed to run with minimal memory requirements.
19. Java Machine Learning Library (Java Machine Learning Library) is a series of related implementations of machine learning algorithms. The algorithms, both source code and documentation, are well written. Its main language is Java.
20. Java-ML is a Java API for a series of machine learning algorithms written in Java. It only provides a standard algorithm interface.
21. MLlib (Spark) is an extensible machine learning library for Apache Spark. Although Java, the library and platform also supports Java, Scala and Python bindings. This library is up to date and has many algorithms.
22. H2O is a machine learning API for intelligent applications. It scales statistics, machine learning, and mathematics on big data. H2O is extensible and developers can use simple mathematics at its core.
23. WalnutiQ is an object-oriented model of part of the human brain, with commonly used learning algorithms in theory (it is researching towards a simple and strong emotional artificial intelligence model).
24. RankLib is a ranking learning algorithm library. Eight popular algorithms have been implemented so far.
25. htm.java (Java-based Hierarchical Temporal Memory algorithm implementation) is a Java interface for the Numenta platform for intelligent computing.
The above are the Java learning tools currently in use. If you find tools that are more practical than this or for other programming languages ​​such as PHP, you can also discuss them together.
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The above introduces 25 Java machine learning tools and libraries, including relevant content. I hope it will be helpful to friends who are interested in PHP tutorials.