Learning in multilayered networks used as autoassociators
IEEE Transactions on Neural Networks
INFORMys: A Flexible Invoice-Like Form-Reader System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autoassociative MLP in Sleep Spindle Detection
Journal of Medical Systems
AANN: an alternative to GMM for pattern recognition
Neural Networks
Learning the parts of objects by auto-association
Neural Networks
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Single-Class Classification with Mapping Convergence
Machine Learning
Application of LVQ to novelty detection using outlier training data
Pattern Recognition Letters
Focusing on non-respondents: Response modeling with novelty detectors
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
SOM-based novelty detection using novel data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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In this paper, we propose an autoassociator-based connectionist model that turns out to be very useful for problems of pattern verification. The model is based on feedforward networks acting as autoassociators trained to reproduce patterns presented at the input to the output layer. The verification is established on the basis of the distance between the input and the output vectors. We give experimental results for assessing the effectiveness of the model for problems of speech verification. The performances were evaluated on DARPA-TIMIT database in continuous speech, using different thresholds and preprocessing schemes, with very promising results.