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- W1546137838 abstract "This paper presents a neural architecture for model breaking detection in real world conditions. This technique use an Independent Component Classifier [1] for detection of unexpected or unknown events in noisy and varying environment. This method is based on subspace classifier [2] and Independant Component Analysis [3]. A feed-forward neural network adapts itself to input evolutions, by detecting novelties, creating and deleting classes. A second process achieves a prototype rotation in order to minimise mutual information of different classes. This synaptic weight evolution rule is based on an anti-hebbian learning rule inspired from neural methods for blind separation of sources [4]. Consequently, under the assumption of statistical independence of different classes, the system is able to detect novelties hidden by simultaneous acoustic events." @default.
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- W1546137838 date "1997-01-01" @default.
- W1546137838 modified "2023-09-28" @default.
- W1546137838 title "Model breaking detection using independent component classifier" @default.
- W1546137838 cites W1991848143 @default.
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- W1546137838 doi "https://doi.org/10.1007/bfb0020213" @default.
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