25 Java machine learning tools and libraries

This list summarizes 25 Java machine learning tools & libraries:
1. Weka integrates machine learning algorithms for data mining work. These algorithms can be applied directly to a dataset or you can write your own code to invoke it. Weka includes a range 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 series of machine learning Algorithms (classification, regression, clustering, anomaly detection, concept drift detection and recommendation system) and evaluation tools. Associated with the Weka project, Moa is also written in Java, its scalability is more strong.


The 3.MEKA project provides an open source implementation for multiple-label learning and evaluation methods. In a multiple-label classification, we want to predict multiple output variables for each input instance. This is not the case with a single target variable in the "normal" case. In addition, Meka is based on the Weka Machine Learning Toolkit.
4. Advanced Data Mining and Machine Learning System (ADAMS) is a new flexible workflow engine designed to quickly build and maintain a real world of complex knowledge flows based on GPLv3 distribution.
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-oriented files. Mallet supports classification algorithms, such as maximum entropy, naive Bayesian 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 for rapid development of machine learning and statistical applications. The core focus of the framework includes a large number of machine learning algorithms as well as statistical testing that can handle a medium-scale dataset.
9. Deeplearning4j is the first commercial-level, open source, distributed in-depth learning library written using Java and Scala. The purpose of the design is to be used in a business environment, rather than as a research tool.
Mahout is a built-in algorithm for machine learning framework. Mahout-samsara helps people create their own math and provides some out-of-the-box algorithmic implementations.
The 11.Rapid miner was developed by the German University of Monterey Technology. 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.
The Apache Samoa is a machine learning (ML) framework that embeds programming abstractions for distributed streaming ML algorithms, and allows the underlying distributed streaming processing engine (dspee, such as Apache Storm, Apache S4, and Apache) to be handled without direct processing SAMZA) The complexity of the case, the development of a new ML algorithm. Users can develop distributed stream ml algorithms and can be executed on multiple dspes.
Neuroph simplifies neural network development by providing Java network libraries and GUI tools that support the creation, training, and preservation of neural networks.
Oryx 2 is a lambda architecture implementation based on Apache Spark and Apache Kafka, but is gradually becoming specialized with real-time large-scale machine learning. This is a framework for building applications, but it also includes packaging and end-to-end applications for collaborative filtering, classification, regression, and clustering.
Stanford classifier is a machine learning tool that can decorating data items to a category. A probability classifier, such as this one, gives the probability distribution of a class assignment to a data item. The software is a Java implementation of the maximum entropy classifier.
16.io is a retina API with fast and accurate natural language processing algorithms like the brain.
17.JSAT is a quick-start machine learning Library. The library was developed in my spare time, based on the GPL3 issue. Some of the content in the library can be learned autonomously, for example, all code is independent. Jsat has no external dependencies and is written in pure java.
N-dimensional Arrays for Java (nd4j) is a scientific computing library for the JVM. They are used in a production environment, which indicates that the routines are designed to run with minimal memory requirements.
The Java Machine Learning Library (Java machine learning Base) is a related implementation of a series of machine learning algorithms. These algorithms, both source code and documentation, are well written. Its main language is java.
JAVA-ML is a Java API that uses Java to write a series of machine learning algorithms. It only provides a standard algorithm interface.
Mllib (Spark) is an extensible machine Learning library for Apache Spark. Although it is Java, the library and the platform also support Java,scala and Python bindings. This library is up to date and has a lot of algorithms.
H2O is a machine learning API for intelligent applications. It has scaled up statistics, machine learning and mathematics on large data. H2O can be extended, and developers can use simple mathematical knowledge in the core section.
Walnutiq is an object-oriented model of the human brain and has a commonly used learning algorithm (which is being studied in the direction of a simple and strong emotional AI model).
Ranklib is a ranking learning algorithm library. Currently, eight popular algorithms have been implemented.
Htm.java (Java-based hierarchical temporal memory algorithm implementation) is a Java interface for the Numenta platform for intelligent computing.
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