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Anomaly Detection with Privileged Information—Part 2

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Manage episode 389861939 series 3474670
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/anomaly-detection-with-privileged-informationpart-2.
Learn how SVDD encapsulates datasets within hyperspheres, and discover how SVDD+ leverages privileged information to optimize training.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #anomaly-detection, #machine-learning, #svdd+, #cvxopt-library, #oneclasssvm, #malware-detection, #quadratic-optimization, #svdd+-vs-oneclasssvm, and more.
This story was written by: @dsmoliakov. Learn more about this writer by checking @dsmoliakov's about page, and for more stories, please visit hackernoon.com.
Explore the intricacies of implementing Support Vector Data Description (SVDD) and its enhanced version, SVDD+, for anomaly detection. Understand how SVDD encapsulates datasets and learn how SVDD+ integrates privileged information in the training phase. Dive into the quadratic optimization process of SVDD+ with a step-by-step guide and insights into the underlying math. Compare SVDD+ with OneClassSVM for anomaly detection and discover the performance on the kdd99 dataset. Gain practical implementation insights using the cvxopt library and navigate the world of anomaly detection with and without privileged information.

  continue reading

126 episodes

Artwork
iconShare
 
Manage episode 389861939 series 3474670
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/anomaly-detection-with-privileged-informationpart-2.
Learn how SVDD encapsulates datasets within hyperspheres, and discover how SVDD+ leverages privileged information to optimize training.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #anomaly-detection, #machine-learning, #svdd+, #cvxopt-library, #oneclasssvm, #malware-detection, #quadratic-optimization, #svdd+-vs-oneclasssvm, and more.
This story was written by: @dsmoliakov. Learn more about this writer by checking @dsmoliakov's about page, and for more stories, please visit hackernoon.com.
Explore the intricacies of implementing Support Vector Data Description (SVDD) and its enhanced version, SVDD+, for anomaly detection. Understand how SVDD encapsulates datasets and learn how SVDD+ integrates privileged information in the training phase. Dive into the quadratic optimization process of SVDD+ with a step-by-step guide and insights into the underlying math. Compare SVDD+ with OneClassSVM for anomaly detection and discover the performance on the kdd99 dataset. Gain practical implementation insights using the cvxopt library and navigate the world of anomaly detection with and without privileged information.

  continue reading

126 episodes

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