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Anomaly Detection with Privileged Information—Part 2

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Manage episode 389861939 series 3474670
Contenuto fornito da HackerNoon. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da HackerNoon o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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

150 episodi

Artwork
iconCondividi
 
Manage episode 389861939 series 3474670
Contenuto fornito da HackerNoon. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da HackerNoon o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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

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