£113.46

Springer - Unsupervised Outlier Detection: Algorithms and Apps

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Description

Expand your knowledge of machine learning with New Developments in Unsupervised Outlier Detection: Algorithms and Applications. Published by Springer, this book provides a deep look into current research regarding unsupervised outlier detection methods. It introduces several new distance-based and density-based outlier scores specifically designed for k-nearest neighbors settings. Readers will explore the latest developments in k-nearest neighbor-based research and gain a better understanding of unsupervised outlier detection in a general context. The text covers specific technical topics including distance-based and density-based detection methods. It also demonstrates how these findings apply to real-world scenarios such as boundary point detection and novel object detection. This book offers a fresh perspective on the field, making it a valuable resource for those studying computer science, AI, and machine learning.

Key Features

Explores new distance-based and density-based outlier scores within a k-nearest neighbors setting.

Provides an updated understanding of general unsupervised outlier detection research.

Covers specialized topics including density-based and distance-based detection methods.

Applies recent findings to practical uses like boundary point detection.

Includes research on novel object detection to expand technical applications.

Offers a new perspective on the current state of k-nearest neighbor research.

Product Specifications

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

Barcode

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