Practical Machine Learning: A New Look at Anomaly Detection (Paperback)

Ted Dunning, Ellen Friedman

  • 出版商: O'Reilly
  • 出版日期: 2014-09-30
  • 售價: $1,080
  • 貴賓價: 9.5$1,026
  • 語言: 英文
  • 頁數: 66
  • 裝訂: Paperback
  • ISBN: 1491911603
  • ISBN-13: 9781491911600
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

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

商品描述(中文翻譯)

尋找您不知道要尋找的資料異常

異常檢測是機器學習中的偵探工作:在大型和複雜的資料集中尋找不尋常的、捕捉詐騙行為、發現奇怪的活動。但是,與福爾摩斯不同,您可能不知道謎題是什麼,更不用說您要尋找的“嫌疑人”是誰。這本 O'Reilly 報告使用實際例子來解釋異常檢測的基本概念如何運作。

從銀行安全到自然科學、醫學和營銷,異常檢測在這個大數據時代有許多有用的應用。隨著物聯網產生更多新型數據,對異常的尋找將變得更加激烈。本報告中描述的概念將幫助您應對自己的異常檢測項目。

- 使用概率模型預測正常情況,並將其與觀察到的情況進行對比
- 使用 t-digest 算法設定自適應閾值,以確定哪些資料超出正常範圍
- 使用更自適應的概率模型來確定複雜系統和信號(如心電圖)中的正常波動
- 使用歷史資料在間歇性事件流(如網路流量)中發現異常
- 學習如何使用預期行為的偏差來觸發詐騙警報