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This page provides an overview of 1 article related to 'machine learning'. Note that filters may be applied to display a sub-set of articles in this category (see FAQs on filtering for usage tips). Select this link to remove all filters.

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Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. (Excerpt from Wikipedia article: Machine learning)

Key statistics

Metadata related to 'machine learning' (as derived from all content tagged with this term):

  • Number of articles referring to 'machine learning': 10 (0.6% of published articles)
  • Total references to 'machine learning' across all Ariadne articles: 18
  • Average number of references to 'machine learning' per Ariadne article: 1.80
  • Earliest Ariadne article referring to 'machine learning': 2003-07
  • Trending factor of 'machine learning': 0 (see FAQs on monitoring of trends)

See our 'machine learning' overview for more data and comparisons with other tags. For visualisations of metadata related to timelines, bands of recency, top authors, and and overall distribution of authors using this term, see our 'machine learning' usage charts. Usage chart icon

Top authors

Ariadne contributors most frequently referring to 'machine learning':

  1. simon choppin (see articles on this topic by this author)
  2. sophia ananiadou (see articles on this topic by this author)
  3. paul watry (see articles on this topic by this author)
  4. michael day (see articles on this topic by this author)
  5. julia chruszcz (see articles on this topic by this author)

Note: Links to all articles by authors listed above set filters to display articles by each author in the overview below. Select this link to remove all filters.

Title Article summary Date

The National Centre for Text Mining: Aims and Objectives

Sophia Ananiadou, Julia Chruszcz, John Keane, John McNaught and Paul Watry describe NaCTeM's plans to provide text mining services for UK academics.

January 2005, issue42, feature article

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