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Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
Learning Object Metadata is a data model, usually encoded in XML, used to describe a learning object and similar digital resources used to support learning. The purpose of learning object metadata is to support the reusability of learning objects, to aid discoverability , and to facilitate their interoperability, usually in the context of ...
Metadata This file contains additional information, probably added from the digital camera or scanner used to create or digitize it. If the file has been modified from its original state, some details may not fully reflect the modified file.
Metaprogramming is a computer programming technique in which computer programs have the ability to treat other programs as their data.It means that a program can be designed to read, generate, analyse, or transform other programs, and even modify itself, while running.
One of the key issues in using learning objects is their identification by search engines or content management systems. [13] This is usually facilitated by assigning descriptive learning object metadata. Just as a book in a library has a record in the card catalog, learning objects must also be tagged with metadata. The most important pieces ...
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
This creates, in effect, a virtual object database that can be used from within the programming language. In object-oriented programming, data-management tasks act on objects that combine scalar values into objects. For example, consider an address book entry that represents a single person along with zero or more phone numbers and zero or more ...
Whereas red–black trees require one bit of balancing metadata per node (the color), AA trees require O(log(log(N))) bits of metadata per node, in the form of an integer "level". The following invariants hold for AA trees: The level of every leaf node is one. The level of every left child is exactly one less than that of its parent.