Artículos relacionados a Practical Machine Learning – A New Look at Anomaly...
Sinopsis
Finding Data Anomalies You Didn't Know to Look For
Anomaly 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" you're looking for. This O'Reilly 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 what's normal and contrast that to what you observe
- Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm
- Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model
- Use historical data to discover anomalies in sporadic event streams, such as web traffic
- Learn how to use deviations in expected behavior to trigger fraud alerts
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for these Apache projects: Spark, Storm, Stratosphere, and Datafu. He contributed to Mahout clustering, classification, and matrix decomposition algorithms and helped expand the new version of Mahout Math library. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, built fraud-detection systems for ID Analytics (LifeLock), and has issued 24 patents to date. Ted has a PhD in computing science from University of Sheffield. When he's not doing data science, he plays guitar and mandolin. Ellen Friedman is a consultant and commentator, currently writing mainly about big data topics. She is a committer for the Apache Mahout project and a contributor to the Apache Drill project. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics including molecular biology, nontraditional inheritance, and oceanography. Ellen is also co-author of a book of magic-themed cartoons, A Rabbit Under the Hat. Ellen is on Twitter at @Ellen_Friedman.
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 15,92
EUR 17,17 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 18,66
EUR 0,66 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoResultados de la búsqueda para Practical Machine Learning – A New Look at Anomaly...
Imagen de archivo
Practical Machine Learning A New Look at Anomaly Detection
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: WO-9781491911600
Cantidad disponible: 3 disponibles
Imagen de archivo
Practical Machine Learning A New Look at Anomaly Detection
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: WO-9781491911600
Cantidad disponible: 3 disponibles
Imagen de archivo
Practical Machine Learning - A New Look at Anomaly Detection
Publicado por
O'Reilly Media, Inc, USA, 2014
ISBN 10: 1491911603
ISBN 13: 9781491911600
Nuevo
Paperback / softback
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 144. Nº de ref. del artículo: B9781491911600
Cantidad disponible: 3 disponibles
Imagen del vendedor
Practical Machine Learning - A New Look at Anomaly Detection
Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Anomaly 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" you're looking for. This O'Reilly 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 what's normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts. Nº de ref. del artículo: LU-9781491911600
Cantidad disponible: 2 disponibles
Imagen del vendedor
Practical Machine Learning - A New Look at Anomaly Detection
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Anomaly 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" you're looking for. This O'Reilly 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 what's normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts. Nº de ref. del artículo: LU-9781491911600
Cantidad disponible: 2 disponibles
Imagen del vendedor
Practical Machine Learning - A New Look at Anomaly Detection
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Anomaly 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" you're looking for. This O'Reilly 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 what's normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts. Nº de ref. del artículo: LU-9781491911600
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Practical Machine Learning: A New Look at Anomaly Detection
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9781491911600_new
Cantidad disponible: 3 disponibles
Imagen del vendedor
Practical Machine Learning - A New Look at Anomaly Detection
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Anomaly 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" you're looking for. This O'Reilly 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 what's normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts. Nº de ref. del artículo: LU-9781491911600
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Practical Machine Learning - A New Look at Anomaly Detection
Publicado por
O?Reilly Media, 2014
ISBN 10: 1491911603
ISBN 13: 9781491911600
Nuevo
Tapa blanda
Original o primera edición
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Calificación del vendedor: 5 de 5 estrellas
Condición: New. This O'Reilly report uses practical example to explain how the underlying concepts of anomaly detection work. Num Pages: 66 pages, colour illustrations. BIC Classification: UY. Category: (XV) Technical / Manuals. Dimension: 152 x 220 x 4. Weight in Grams: 112. . 2014. 1st Edition. Paperback. . . . . Nº de ref. del artículo: V9781491911600
Cantidad disponible: 1 disponibles
Imagen de archivo
Practical Machine Learning - A New Look at Anomaly Detection
Publicado por
O'Reilly Media, Inc, USA, 2014
ISBN 10: 1491911603
ISBN 13: 9781491911600
Nuevo
Paperback / softback
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. Nº de ref. del artículo: C9781491911600
Cantidad disponible: Más de 20 disponibles