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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
15 years 8 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
ECML
2003
Springer
15 years 8 months ago
Robust k-DNF Learning via Inductive Belief Merging
A central issue in logical concept induction is the prospect of inconsistency. This problem may arise due to noise in the training data, or because the target concept does not fit...
Frédéric Koriche, Joël Quinquet...
132
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NIPS
1998
15 years 4 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
NIPS
2003
15 years 4 months ago
Learning to Find Pre-Images
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solu...
Gökhan H. Bakir, Jason Weston, Bernhard Sch&o...
ILP
1997
Springer
15 years 7 months ago
A Logical Framework for Graph Theoretical Decision Tree Learning
Abstract. We present a logical approach to graph theoretical learning that is based on using alphabetic substitutions for modelling graph morphisms. A classi ed graph is represente...
Peter Geibel, Fritz Wysotzki