Abstract
The phenomenal behaviour and composition of human cognition is yet to be defined comprehensibly. Developing the same, artificially, is a foremost research area in artificial intelligence and related fields. In this chapter we look at advances made in the unsupervised learning paradigm (self organising methods) and its potential in realising artificial cognitive machines. The first section delineates intricacies of the process of learning in humans with an articulate discussion of the function of thought and the function of memory. The self organising method and the biological rationalisations that led to its development are explored in the second section. The next focus is the effect of structure restrictions on unsupervised learning and the enhancements resulting from a structure adapting learning algorithm. Generation of a hierarchy of knowledge using this algorithm will also be discussed. Section four looks at new means of knowledge acquisition through this adaptive unsupervised learning algorithm while the fifth examines the contribution of multimodal representation of inputs to unsupervised learning. The chapter concludes with a summary of the extensions outlined.
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
Alahakoon, D., Halgamuge, S., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions On Neural Networks (2000)
Amarasiri, R., Alahakoon, D., Smith, K.: Applications of the growing self organizing map on high dimensional data. In: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (2006)
Amarasiri, R., Wickremasinghe, L., Alahakoon, D.: Enhanced Cluster Visualization Using the Data Skeleton Model. In: Proceedings of the Third International Conference on Intelligent Systems Design and Application (2003)
Alahakoon, D.: Controlling the spread of dynamic self-organising maps. Neural Computing & Applications (2004)
De Silva, D., Alahakoon, D.: Analysis of Seismic Activity using the Growing SOM for the Identification of Time Dependent Patterns. In: Proceedings of the Second International Conference on Information and Automation for Sustainability (2006)
Hsu, A., Tang, S., Halgamuge, S.: An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data. International Journal Of Bioinformatics (2003)
Barnes, J. (ed.): Aristotle (IV BC) Complete Works of Aristotle. Princeton University Press, Princeton (1992)
Field, D.J.: What is the goal of sensory encoding? Neural Computation 6 (1994)
Gilbert, C.D., Hirsch, J.A., Wiesel, T.N.: Lateral interactions in visual cortex. In: Cold Spring Harbour Symposia on Quantitative Biology. Cold Spring Harbour Press, LV (1990)
Hirsh, H., Spinelli, D.: Visual experience modifies distribution of horizontally and vertically oriented receptive fields in cats. Science 68, 869–871 (1970)
Kant, I.: Critique of Practical Reason, translated by J.H Bernard (1986) Hafner (1788)
Kohonen, T.: Self-Organising Maps. Springer, New York (1995)
Minsky, M.: Emotion Machine. Simon and Schuster, New York (2006)
Perlovsky, L.: Cognitive high level information fusion. Information Sciences (2007)
Perlovsky, L.: Towards physics of mind: Concepts, emotions, consciousness and symbols. Physics of Life (2006)
Rolls, E.T., Treves, A.: Neural Networks and Brain Function. Oxford University Press, Oxford (1999)
Rolls, E.T.: A model of the operation of the hippocampus and entorhinal cortex in memory. International Journal of Neural Systems (1995)
Tulving, E.: Organisation of memory: Quo Vardis. In: Memory: Systems, Process or Function? Oxford University Press, Oxford (1999)
Tulving, E.: Elements of Episodic Memory. Clarendon, Oxford (1983)
Vernon, D., Metta, G., Sandini, G.: A Survey of Artificial Cognitive Systems. IEEE Transactions on Evolutionary Computation, Special Issue on Autonomous Mental Development (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
De Silva, D., Alahakoon, D., Dharmage, S. (2009). Extensions to Knowledge Acquisition and Effect of Multimodal Representation in Unsupervised Learning. In: Hassanien, AE., Abraham, A., Vasilakos, A.V., Pedrycz, W. (eds) Foundations of Computational, Intelligence Volume 1. Studies in Computational Intelligence, vol 201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01082-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-01082-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01081-1
Online ISBN: 978-3-642-01082-8
eBook Packages: EngineeringEngineering (R0)