Practical machine learning : a new look at anomaly detection /

Annotation

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Dunning, Ted, 1956-
Otros Autores: Friedman, B. Ellen
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, 2014.
Temas:
Machine learning.
Anomaly detection (Computer security)
Apprentissage automatique.
Détection d'anomalies (Sécurité informatique)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Practical machine learning :  |b a new look at anomaly detection /  |c Ted Dunning, Ellen Friedman. 
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520 8 |a Annotation  |b Finding Data Anomalies You Didn't Know to Look ForAnomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what suspects youre looking for. This OReilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project. Use probabilistic models to predict whats normal and contrast that to what you observeSet an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithmEstablish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic modelUse historical data to discover anomalies in sporadic event streams, such as web trafficLearn how to use deviations in expected behavior to trigger fraud alerts. 
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700 1 |a Friedman, B. Ellen. 
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