Learning algorithms


Ontology type: npg:Subject  | skos:Concept     


Concept Info

NAME

Learning algorithms

DESCRIPTION

A learning algorithm is a mathematical framework or procedure that calculates the best output given a particular set of data. It does this by updating the calculation based on the difference between the actual and desired output. These algorithms are typically concerned with representation and generalization of the input data.

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HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/ontologies/subjects/learning-algorithms'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/ontologies/subjects/learning-algorithms'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/ontologies/subjects/learning-algorithms'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/ontologies/subjects/learning-algorithms'


 

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8 skos:inScheme sg:ontologies/subjects/
9 skos:prefLabel Learning algorithms
10 sg:ontologies/subjects/ dcterms:description The Nature Subjects Taxonomy is a polyhierarchical categorization of scholarly subject areas which are used for the indexing of content by Springer Nature.
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