Titlebook: Representation in Machine Learning; M. N. Murty,M. Avinash Book 2023 The Author(s), under exclusive license to Springer Nature Singapore P

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2191-5768 ues using benchmark datasets.Illustrates the content using e.This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfort

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Representation Using Linear Combinations, be viewed as a special case of the linear representation schemes. We specifically consider, in this chapter, various types of feature selection schemes, principal components, random projections and non-negative matrix factorization.

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Representation in Machine Learning978-981-19-7908-8Series ISSN 2191-5768 Series E-ISSN 2191-5776

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Introduction,In this chapter we provide a brief introduction to machine learning (.). We introduce some of the important steps in . including data acquisition, feature engineering, model selection, model estimation, model evaluation, and model explanation.