25 Java machine learning tools and libraries
It industry more and more fire, with more new troops to join the IT family, the proportion of Java is also more and more large, the following for everyone to organize a number of learning tools.
1. Weka integrates a machine learning algorithm 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.
The 2.Massive Online analysis (MOA) is a popular open source framework for data stream mining and has a very active growth community. It includes a range of machine learning algorithms (classification, regression, clustering, anomaly detection, concept drift detection and referral systems), and evaluation tools. Associated with the Weka project, Moa is also written in Java, which is more extensible.
The 3.MEKA project provides an open source implementation for multi-label learning and evaluation methods. In the multi-label category, we want to predict multiple output variables for each input instance. This differs from the case where only one single target variable is involved 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 type of flexible workflow engine designed to quickly establish and maintain a complex knowledge stream of the real world, 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-oriented machine learning toolkit for text-based 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 rapid development of machine learning and statistical applications. The core focus of the framework is a large number of machine learning algorithms and statistical testing that can handle medium-sized datasets.
9. Deeplearning4j is the first commercially-available, open-source, distributed in-depth learning library written in Java and Scala. It is designed to be used in a business environment, not as a research tool.
Mahout is a machine learning framework with built-in algorithms. Mahout-samsara helps people create their own math and provides some ready-made algorithm implementations.
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 a number of machine learning algorithms for data processing, visualization, and modeling.
Apache Samoa is a machine learning (ML) framework that embeds programming abstractions for distributed stream ML algorithms and allows the underlying distributed stream processing engine (Dspee such as Apache Storm, Apache S4, and Apache) to be processed without direct processing SAMZA) in case of complexity, develop a new ML algorithm. The user can develop a distributed stream ml algorithm and can execute 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 built on Apache Spark and Apache Kafka, but gradually began to specialize with real-time, large-scale machine learning. This is a framework for building applications, but it also includes packaging, as well as end-to-end applications for collaborative filtering, classification, regression, and clustering.
Stanford classifier is a machine learning tool that enables data items to be collocated into a category. A probability classifier, such as this one, can give a probability distribution of the class allocation for a data item. The software is a Java implementation of the maximum entropy classifier.
16.io is a retina API that has a fast and accurate natural language processing algorithm similar to the brain.
17.JSAT is a quick-start machine learning Library. The library was developed in my spare time, based on the GPL3 release. Some of the content in the library can be self-learning, 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 is a series of related implementations 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 a series of machine learning algorithms written in Java. It only provides a standard algorithm interface.
MLlib (Spark) is the 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 many algorithms.
H2O is a machine learning API for smart applications. It has scaled statistics, machine learning, and mathematics on big data. H2O can be extended, and developers can use simple mathematical knowledge in the core section.
Walnutiq is a part of the human brain object-oriented model, with a common theory of learning algorithms (in the direction of a simple 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.
The above is the use of more Java learning tools, if you find more useful than this or other programming languages such as PHP tools, can also be discussed together AH.
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The above describes 25 Java machine learning tools and libraries, including aspects of the content, I hope that the PHP tutorial interested in a friend helpful.
