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Anomaly Detection Principles and Algorithms (Terrorism, Security, and Computation)
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XCD 339
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Anomaly detection principles and algorithms provide a readable and elegant presentation of the principles of anomaly detection, making it an easy introduction for newcomers to the field.
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Product Details
| Publisher | Springer |
| Publication date | January 25, 2018 |
| Edition | 2017th |
| Language | English |
| Print length | 239 pages |
| ISBN-10 | 3319675249 |
| ISBN-13 | 978-3319675244 |
| Item Weight | 1.35 pounds (610 grams) |
| Dimensions | 6.25 x 0.75 x 9.25 inches (15.9 x 1.9 x 23.5 cm) |
| Part of series | Terrorism, Security, and Computation |
Who Should Buy?
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Security Analysts
Security analysts will benefit from practical insights on detecting threats using advanced anomaly detection techniques.
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Data Scientists
Data scientists involved in security research can utilize algorithms detailed in the book for anomaly detection applications.
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Academic Researchers
Academic researchers focusing on security and computation can leverage the comprehensive theoretical frameworks discussed.
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General Readers
Individuals without a technical background may find the content overly complex and difficult to understand.
Product Description
Anomaly Detection Principles and Algorithms (Terrorism, Security, and Computation)
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Computer Vision & Pattern Recognition Editorial Review
The book provides a solid foundation on anomaly detection from a mathematical perspective. However, it lacks practical examples and is not suitable for teaching at the undergraduate level. It is a useful resource for projects and includes a comprehensive bibliography.
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Pros
- Provides essential concepts and math formulas
- Clear and well-written
- Includes useful algorithms
- Explains theory in a simple way
- Comprehensive bibliography
Cons
- Lacks practical examples
Product Price History
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XCD 339
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Features & Benefits
- The book offers a readable and elegant presentation of anomaly detection principles.
- It provides an easy introduction for newcomers to the field of anomaly detection.
- A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses.
- The book covers algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data.
- New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data.
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