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Handbook of Anomaly Detection: Cutting-edge Methods and Hands-On Code Examples

Data Mining

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Buy the Paperback – Get the Complimentary Digital Edition Purchase the paperback edition and enjoy a complimentary digital edition on your favorite device—yours to keep for personal use. The print edition adopts the classic Springer font and layout for pleasing reading. The digital edition adopts a beautiful Latex layout for pleasing reading. What's new in the 2nd edition? Comprehensive and in-depth coverage: the 2nd edition offers a well-rounded understanding of the foundational concepts. Each chapter builds on the previous one, creating a cohesive and comprehensive learning experience. Enhanced explanations and visual presentations: the 2nd edition is enriched with a wealth of visualizations to deliver complex concepts. You’ll learn not just the ‘how’ but also the ‘why’ behind each method. Supervised and unsupervised learning techniques: the 2nd edition covers advanced machine learning techniques for anomaly detection. On unsupervised learning, you will be equipped with a wide range of modern techniques. On supervised learning, you will gain a deeper understanding into grid-search, hyperparameter tuning, regularization, under-sampling and over-sampling techniques. 200+ data science Q&A: by working through the Q&A, you’ll be well-prepared to showcase your expertise and secure your next job opportunity. Developer review PyOD has been successfully used in numerous academic types of research and commercial products with more than 10 million downloads since 2017 “Anomaly detection is a critical technique to identify rare items in risk modeling, security, and healthcare. Dr. Kuo's book provides hands-on guidance on how to use existing tools, such as PyOD, to leverage this technique in your daily data science work. More importantly, this book reviews more than 10 leading detection algorithms, with both algorithm descriptions and detailed code examples.” ~ Yue Zhao, Principal Developer of PyOD Who this book Is for This book is for data science professionals, developers, and students who are searching for anomaly detection methods or want to know about any new development in this field. Data science professionals in fields such as business, finance, insurance, engineering, science, or medical, will gain a deep understanding of the practices. Table of Contents Ch 1: Introduction outlines Ch 2: Histogram-Based Outlier Score (HBOS) Ch 3: Empirical Cumulative Distribution-based Outlier Detection (ECOD) Ch 4: Isolated Forest Ch 5: Principal Component Analysis (PCA) Ch 6: One-Class Support Vector Machine (OC-SVM) Ch 7: Gaussian Mixture Model (GMM) Ch 8: K-nearest Neighbors (KNNs) Ch 9: Local Outlier Factor (LOF) Ch 10: Cluster-Based Local Outlier Factor (CBLOF) Ch 11: Autoencoders Ch 12: Supervised Learning Primer Ch 13: Regularization Ch 14: Sampling Techniques for Extremely Imbalanced Data Ch 15: Representation Learning for Outlier Detection

Product Specifications

Format: paperback
ASIN: B0DK6WPWZY
Category: Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
Domain: Amazon UK
Release Date: 17 October 2024
Listed Since: 17 October 2024

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Handbook of Anomaly Detection: Cutting-edge Methods and Hands-On Code Examples

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