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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
13 years 10 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
GECCO
2005
Springer
126views Optimization» more  GECCO 2005»
13 years 10 months ago
Is negative selection appropriate for anomaly detection?
Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algori...
Thomas Stibor, Philipp H. Mohr, Jonathan Timmis, C...
ICIAP
2005
ACM
14 years 4 months ago
Learning Intrusion Detection: Supervised or Unsupervised?
Abstract. Application and development of specialized machine learning techniques is gaining increasing attention in the intrusion detection community. A variety of learning techniq...
Pavel Laskov, Patrick Düssel, Christin Sch&au...
NIPS
1997
13 years 5 months ago
A Framework for Multiple-Instance Learning
Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many ins...
Oded Maron, Tomás Lozano-Pérez
TIP
2008
169views more  TIP 2008»
13 years 4 months ago
Weakly Supervised Learning of a Classifier for Unusual Event Detection
In this paper, we present an automatic classification framework combining appearance based features and Hidden Markov Models (HMM) to detect unusual events in image sequences. One...
Mark Jager, Christian Knoll, Fred A. Hamprecht