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» Learning Circuits with few Negations
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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
13 years 9 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai
IR
2008
13 years 4 months ago
Negation recognition in medical narrative reports
Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often...
Lior Rokach, Roni Romano, Oded Maimon
DFT
2008
IEEE
182views VLSI» more  DFT 2008»
13 years 6 months ago
Hardware Trojan Detection and Isolation Using Current Integration and Localized Current Analysis
This paper addresses a new threat to the security of integrated circuits (ICs). The migration of IC fabrication to untrusted foundries has made ICs vulnerable to malicious alterat...
Xiaoxiao Wang, Hassan Salmani, Mohammad Tehranipoo...
STOC
2006
ACM
159views Algorithms» more  STOC 2006»
14 years 4 months ago
Learning a circuit by injecting values
d Abstract] Dana Angluin James Aspnes Jiang Chen Yinghua Wu We propose a new model for exact learning of acyclic circuits using experiments in which chosen values may be assigned ...
Dana Angluin, James Aspnes, Jiang Chen, Yinghua Wu
ALT
2000
Springer
14 years 1 months ago
Learning Recursive Concepts with Anomalies
This paper provides a systematic study of inductive inference of indexable concept classes in learning scenarios in which the learner is successful if its final hypothesis describ...
Gunter Grieser, Steffen Lange, Thomas Zeugmann