Machine Learning Algorithms

Machine Learning Algorithms

Namrata Dhanda (Amity University, India), Stuti Shukla Datta (Amity University, India) and Mudrika Dhanda (Royal Holloway University, UK)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/978-1-5225-7955-7.ch009
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Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.
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Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E” (Mitchell, 1997). This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper “Computing Machinery and Intelligence”, in which the question “Can machines think?” is replaced with the question “Can machines do what we (as thinking entities) can do?” (Turing, 2009). In Turing's proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed.

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