Innovative Applications of Machine Learning in Image Recognition

Ke Qian

doi:10.70393/6a6374616d.323533

Authors

Ke Qian University of Southern California

DOI:

https://doi.org/10.70393/6a6374616d.323533

ARK:

https://n2t.net/ark:/40704/JCTAM.v2n1a03

Disciplines:

Artificial Intelligence and Intelligence

Subjects:

Machine Learning

References:

18

Keywords:

Machine Learning, Image Recognition, Classification

Abstract

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. This paper aims to devle into the scope of applications of Machine Learning in image recgonition.

Author Biography

Ke Qian, University of Southern California

University of Southern California, USA.

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