MACHINE LEARNING IN JAVA

Many tasks today are better and cheaper to solve not with the help of a huge staff of expensive specialists, but using machine learning technologies. This is true for both big data businesses and decision-making activities with limited time and information.

Machine learning is a subdivision of artificial intelligence that studies methods for creating self-learning algorithms. Machine learning software products involve the use of algorithms that allow a program to create its own algorithms for performing certain functions and change them to optimize the performance of these functions. The main problem of computers in the past was their inability to act flexibly in a non-standard situation, which negated the huge speed advantage over humans. Machine learning is a kind of “bridge” between a person’s ability to improvise and a high-speed program.

The implementation of machine learning technologies is based on statistical, mathematical and probabilistic methods – that is, a computer evaluates each unique situation by comparing it with a huge database that contains more or less similar cases.

The main task of the programmer is to optimize the ability to select the most relevant cases from the database and to constantly increase this database.

Thanks to this, machine learning technologies can be used in many areas that require quick decision making in a situation with incomplete data – that is, the business area and, above all, the stock market. Machine learning technologies are widely adopted in many areas. For example, transport management: both individual transport units and entire logistics systems. Many logistics companies are already implementing fully automated and self-learning warehouse systems and order processing services.

Based on machine learning, the system for recognizing faces, objects and images opens up a very wide range of possibilities. From face recognition in security and access control systems to diagnostics of mental and neurological diseases, from assessing yields or stock balances to using in crime investigations – all this is done by self-learning programs, including those written in the Java language.

Many programming languages ​​are used in the field of artificial intelligence and machine learning. But Java, despite its quarter-century history, is still one of the most popular in this area. It is also successfully used for programming neural networks, search algorithms, and robotics systems. For the purposes of building machine learning algorithms, Java has two main advantages. This is the highest scalability, which means the ability to operate on huge amounts of data and process several requests in parallel, and object-orientation, which sets the programming logic suitable for solving machine learning problems.

The ability of applications created in Java to run on any platform is also beneficial. The language allows you to encode algorithms of various types; the code written in it is easy for later debugging by third-party developers. The Standard Widget Toolkit allows you to create easy-to-use user interfaces for working with Java machine learning applications.

Java is used to implement a wide variety of machine learning projects, including:

• artificial intelligence libraries;

• expert systems that emulate a person’s ability to make decisions;
• neural networks;
• natural language processing tools – speech recognizers and processors, handwritten texts, lip reading programs, etc.

The main features of Java for solving machine learning problems is the presence of highly specialized tools for solving specific problems – libraries and frameworks, working with which requires high programming skills and constant development. For example, these include the popular Weka library, which contains many ready-made machine learning algorithms for data analysis.

This library provides developers with the ability to solve problems of classification, regression analysis, data clustering. The library implements such learning algorithms as support vector machines, decision trees, logistic regression, feature selection algorithms, filtering methods, etc.

To solve machine learning problems in the field of natural language analysis, data visualization and others, the Smile framework is used, the ELKI framework for KDD applications is used to work with data in a relational database, and the Java-ML library allows solving clustering and function selection problems.

Programming machine learning algorithms in Java is a tricky and complex task, even for experienced programmers. Implementation of small and medium-scale projects already requires the involvement of a whole development team, and in addition, an integrated approach to quality management and testing of ready-made machine learning applications in Java is required.

Therefore, if you have a task to create software for machine learning and working with big data in any field – from business to medicine, from security to education – it is best to turn to professionals. Our portfolio includes ready-made successful machine learning projects. We are constantly developing and open for cooperation!