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This book enriches unsupervised outlier detection research by
proposing several new distance-based and density-based outlier
scores in a k-nearest neighbors' setting. The respective chapters
highlight the latest developments in k-nearest neighbor-based
outlier detection research and cover such topics as our present
understanding of unsupervised outlier detection in general;
distance-based and density-based outlier detection in particular;
and the applications of the latest findings to boundary point
detection and novel object detection. The book also offers a new
perspective on bridging the gap between k-nearest neighbor-based
outlier detection and clustering-based outlier detection, laying
the groundwork for future advances in unsupervised outlier
detection research. The authors hope the algorithms and
applications proposed here will serve as valuable resources for
outlier detection researchers for years to come.
This book advances research on mobile robot localization in unknown
environments by focusing on machine-learning-based natural scene
recognition. The respective chapters highlight the latest
developments in vision-based machine perception and machine
learning research for localization applications, and cover such
topics as: image-segmentation-based visual perceptual grouping for
the efficient identification of objects composing unknown
environments; classification-based rapid object recognition for the
semantic analysis of natural scenes in unknown environments; the
present understanding of the Prefrontal Cortex working memory
mechanism and its biological processes for human-like localization;
and the application of this present understanding to improve mobile
robot localization. The book also features a perspective on
bridging the gap between feature representations and
decision-making using reinforcement learning, laying the groundwork
for future advances in mobile robot navigation research.
This book advances research on mobile robot localization in unknown
environments by focusing on machine-learning-based natural scene
recognition. The respective chapters highlight the latest
developments in vision-based machine perception and machine
learning research for localization applications, and cover such
topics as: image-segmentation-based visual perceptual grouping for
the efficient identification of objects composing unknown
environments; classification-based rapid object recognition for the
semantic analysis of natural scenes in unknown environments; the
present understanding of the Prefrontal Cortex working memory
mechanism and its biological processes for human-like localization;
and the application of this present understanding to improve mobile
robot localization. The book also features a perspective on
bridging the gap between feature representations and
decision-making using reinforcement learning, laying the groundwork
for future advances in mobile robot navigation research.
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