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Springer - Unsupervised Outlier Detection Algorithms Book

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Description

This Springer publication advances the field of unsupervised outlier detection research. It introduces several new distance-based and density-based outlier scores within a k-nearest neighbors setting. The text provides a comprehensive look at current understanding of unsupervised outlier detection methods in general, with a specific focus on distance and density-based approaches. Readers can explore the latest developments in k-nearest neighbor-based research and see how these findings apply to practical tasks like boundary point detection and novel object detection. The book offers a fresh perspective on bridging gaps in the field, making it a valuable resource for those studying AI and machine learning applications.

Key Features

This Springer book introduces new distance-based and density-based outlier scores in a k-nearest neighbors setting.

The chapters cover the current state of unsupervised outlier detection research and general principles.

Content includes specific details on distance-based and density-based outlier detection methods.

The text applies recent findings to the practical areas of boundary point detection and novel object detection.

This research resource provides a new perspective on bridging existing gaps in outlier detection studies.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
25 November 2020
Listed Since
10 September 2020

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No barcode data available

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