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Mining with Rarity ? A Unifying Framework

Mining with Rarity ? A Unifying Framework

Mining with Rarity ? A Unifying Framework. Advisor ? Dr. Hsu Reporter ? Chun Kai Chen Author ? Gary M. Weiss. ACM SIGKDD 2004 Explorations Issues Vol 6, Issue 1, 7-19. Outline. Motivation Objective Introduction Why does rarity make data mining difficult Methods for addressing rarity

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383 views • 38 slides
Gene Expression

Gene Expression

Gene Expression. AKA Molecular Biology. Gene Expression Le Dogma Centrale. Genome Organization. Gene Structure. Transcription. Post-transcriptional Modification. Translation. Post-translational Modification. Regulatory Sequences. Transcription.

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393 views • 27 slides
Prioritizing what to work on: Spam classification example

Prioritizing what to work on: Spam classification example

Prioritizing what to work on: Spam classification example. Machine learning system design. Machine Learning. Building a spam classifier. From: cheapsales@buystufffromme.com To: ang@cs.stanford.edu Subject: Buy now! Deal of the week! Buy now! Rolex w4tchs - $100 Med1cine (any kind) - $50

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306 views • 18 slides
Optimizing F-Measure with Support Vector Machines

Optimizing F-Measure with Support Vector Machines

Optimizing F-Measure with Support Vector Machines. David R. Musicant Vipin Kumar Aysel Ozgur FLAIRS 2003 Tuesday, May 13, 2003. Carleton College. Overview. Classification algorithms often evaluated by test set accuracy

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185 views • 18 slides
Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell

Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell

Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell. NO Labeled Examples NIPS are Rare Classes. How to Find NIPS?. Main Theorem : If 1) ;

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139 views • 1 slides

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