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  2. Encog - Wikipedia

    en.wikipedia.org/wiki/Encog

    Encog is a machine learning framework available for Java and .Net. [1] Encog supports different learning algorithms such as Bayesian Networks, Hidden Markov Models and Support Vector Machines. However, its main strength lies in its neural network algorithms. Encog contains classes to create a wide variety of networks, as well as support classes ...

  3. Deeplearning4j - Wikipedia

    en.wikipedia.org/wiki/Deeplearning4j

    Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). [ 2 ] [ 3 ] It is a framework with wide support for deep learning algorithms. [ 4 ] Deeplearning4j includes implementations of the restricted Boltzmann machine , deep belief net , deep autoencoder, stacked denoising autoencoder and recursive ...

  4. Real-time Java - Wikipedia

    en.wikipedia.org/wiki/Real-Time_Java

    Real-time Java is a catch-all term for a combination of technologies that enables programmers to write programs that meet the demands of real-time systems in the Java programming language. Java's sophisticated memory management , native support for threading and concurrency, type safety , and relative simplicity have created a demand for its ...

  5. Encapsulation (computer programming) - Wikipedia

    en.wikipedia.org/wiki/Encapsulation_(computer...

    Essentially, encapsulation prevents external code from being concerned with the internal workings of an object. Encapsulation allows developers to present a consistent interface that is independent of its internal implementation. As one example, encapsulation can be used to hide the values or state of a structured data object inside a class.

  6. Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Supervised_learning

    Active learning: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.

  7. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...

  8. Field encapsulation - Wikipedia

    en.wikipedia.org/wiki/Field_encapsulation

    In computer programming, field encapsulation involves providing methods that can be used to read from or write to the field rather than accessing the field directly. Sometimes these accessor methods are called getX and setX (where X is the field's name), which are also known as mutator methods.

  9. Data encapsulation - Wikipedia

    en.wikipedia.org/wiki/Data_encapsulation

    Data encapsulation, also known as data hiding, is the mechanism whereby the implementation details of a class are kept hidden from the user. The user can only perform a restricted set of operations on the hidden members of the class by executing special functions commonly called methods to prevent attributes of objects from being easily viewed and accessed.