Abstract
We present an approach to constructing a model of the universe for explaining observations and making decisions based on learning new concepts. We use a weak statistical model, e.g. a discriminative classifier, to distinguish errors in measurements from improper modeling. We use boolean logic to combine outcomes of direct detectors of relevant events, e.g. presence of sound and presence of human shape in the field of view, into more complex models explaining the states in which the universe may appear. The process of constructing a new concept is initiated when a significant disagreement - incongruence - has been observed between incoming data and the current model of the universe. Then, a new concept, i.e. a new direct detector, is trained on incongruent data and combined with existing models to remove the incongruence.We demonstrate the concept in an experiment with human audio-visual detection.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Popper, K.R.: The Logic of Scientific Discovery. Routledge, New York (1995)
Pavel, M., Jimison, H., Weinshall, D., Zweig, A., Ohl, F., Hermansky, H.: Detection and identification of rare incongruent events in cognitive and engineering systems. Dirac white paper, OHSU (2008)
Weinshall, D., et al.: Beyond novelty detection: Incongruent events, when general and specific classifiers disagree. In: NIPS 2008, pp. 1745–1752 (2008)
Halmos, P.R.: Lectures on Boolean Algebras. Springer, Heidelberg (1974)
Franc, V., Sonneburg, S.: Optimized cutting plane algorithm for large-scale risk minimization. Journal of Machine Learning Research 10, 2157–2232 (2009)
Pajdla, T., Havlena, M., Heller, J., Kayser, H., Bach, J.H., Anemüller, J.: Incongruence detection for detecting, removing, and repairing incorrect functionality in low-level processing. Research Report CTU–CMP–2009–19, Center for Machine Perception, K13133 FEE Czech Technical University (2009)
Wikipedia: Horn Clause (2010), http://en.wikipedia.org/wiki/Horn_clause
Wikipedia: PROLOG (2010), http://en.wikipedia.org/wiki/Prolog
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Pajdla, T., Havlena, M., Heller, J. (2012). Learning from Incongruence. In: Weinshall, D., Anemüller, J., van Gool, L. (eds) Detection and Identification of Rare Audiovisual Cues. Studies in Computational Intelligence, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24034-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-24034-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24033-1
Online ISBN: 978-3-642-24034-8
eBook Packages: EngineeringEngineering (R0)