English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Statistical Machine Learning (SML) refers to a body of algorithms and methodsby which computers are allowed to discover important features of input datasets which are often very large in size. The very task of feature discoveryfrom data is essentially the meaning of the keyword `learning' in SML.Theoretical justifications for the effectiveness of the SML algorithms areunderpinned by sound principles from different disciplines, such as ComputerScience and Statistics. The theoretical underpinnings particularly justified bystatistical inference methods are together termed as statistical learningtheory.  This paper provides a review of SML from a Bayesian decision theoretic pointof view -- where we argue that many SML techniques are closely connected tomaking inference by using the so called Bayesian paradigm. We discuss manyimportant SML techniques such as supervised and unsupervised learning, deeplearning, online learning and Gaussian processes especially in the context ofvery large data sets where these are often employed. We present a dictionarywhich maps the key concepts of SML from Computer Science and Statistics. Weillustrate the SML techniques with three moderately large data sets where wealso discuss many practical implementation issues. Thus the review isespecially targeted at statisticians and computer scientists who are aspiringto understand and apply SML for moderately large to big data sets.

A Bayesian Perspective of Statistical Machine Learning for Big Data