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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 ...
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 ...
It provides a means to associate metadata with a class where the language does not have explicit support for such metadata. To use this pattern, a class implements a marker interface [ 1 ] (also called tagging interface ) which is an empty interface, [ 2 ] and methods that interact with instances of that class test for the existence of the ...
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 ...
Sorted into folders by class of events as well as metadata in a JSON file and annotations in a CSV file. 1,059 Sound Classification 2014 [146] [147] J. Salamon et al. AudioSet 10-second sound snippets from YouTube videos, and an ontology of over 500 labels. 128-d PCA'd VGG-ish features every 1 second. 2,084,320
Objects and collections of objects similar to what would be found in a Smalltalk program for messages and parameters. Managers similar to IBM i Objects, such as a directory to files and files consisting of metadata and records. Managers conceptually provide memory and processing resources for their contained objects.
PMML provides a way for analytic applications to describe and exchange predictive models produced by data mining and machine learning algorithms. It supports common models such as logistic regression and other feedforward neural networks. Version 0.9 was published in 1998. [1] Subsequent versions have been developed by the Data Mining Group.
First of all, a concept is a simple version of a Unified Modeling Language (UML) class. The class definition [1] is adopted to define a concept, namely: a set of objects that share the same attributes, operations, relations, and semantics. The following concept types are specified: STANDARD CONCEPT: a concept that contains no further (sub ...