Abstract
With the development of computer vision, robots need to recognize targets from image sequences by supervised learning for autonomous navigation. To identify objects, the percept learning system of autonomous robots using unsupervised learning presented in the last part has segmented the images into nonoverlapping but meaningful regions based on low-level features such as color, texture measures and so on. In this chapter, we give an overview of the field of supervised learning for data classification in general and for object recognition tasks from the perspective of an instance-based classifier in particular. We begin with an introduction to the concepts of classification, followed by instance-based classification methods in specific. The semi-supervised learning is next described. Finally, the performance evaluation metrics are reviewed.
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Wang, X., Wang, X., Wilkes, D.M. (2020). Supervised Learning for Data Classification Based Object Recognition. In: Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment. Springer, Singapore. https://doi.org/10.1007/978-981-13-9217-7_9
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DOI: https://doi.org/10.1007/978-981-13-9217-7_9
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