Machine Learning and Pattern Recognition in Affective Computing

Ramón Zatarain Cabada, Héctor Manuel Cárdenas López and Hugo Jair Escalante
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Ramón Zatarain Cabada: Instituto Tecnológico de Culiacán
Héctor Manuel Cárdenas López: Instituto Tecnológico de Culiacán
Hugo Jair Escalante: Instituto Nacional de Astrofísica

Chapter Chapter 2 in Multimodal Affective Computing, 2023, pp 21-33 from Springer

Abstract: Abstract Machine learning (ML) and pattern recognition are at the core of affective computing, as most tasks can be formulated as machine learning problems (e.g., recognition, clustering, prediction, forecasting, etc.).This chapter provides an introduction to ML. The goal of this chapter is to provide an overview of field, describing the main techniques that are used within affective computing and outlines current trends, aiming to make the book as self-contained as possible. We first introduce the learning problem and provide an overview of the main data modalities considered in affective computing. Then we describe the main ML variants and provide an overview of traditional techniques. Next, we present a section devoted to dimensionality reduction. Furthermore, we review learning methods based on deep learning. Finally, a brief discussion of the current trends is provided.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-32542-7_2

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DOI: 10.1007/978-3-031-32542-7_2

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