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Machine Learning and Pattern Recognition in Affective Computing

In: Multimodal Affective Computing

Author

Listed:
  • Ramón Zatarain Cabada

    (Instituto Tecnológico de Culiacán)

  • Héctor Manuel Cárdenas López

    (Instituto Tecnológico de Culiacán)

  • Hugo Jair Escalante

    (Instituto Nacional de Astrofísica)

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.

Suggested Citation

  • Ramón Zatarain Cabada & Héctor Manuel Cárdenas López & Hugo Jair Escalante, 2023. "Machine Learning and Pattern Recognition in Affective Computing," Springer Books, in: Multimodal Affective Computing, chapter 0, pages 21-33, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-32542-7_2
    DOI: 10.1007/978-3-031-32542-7_2
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