Artefactual structure from least squares multidimensional scaling

We consider the problem of illusory or artefactual structure from the visualisation of high-dimensional structureless data. In particular we examine the role of the distance metric in the use of topographic mappings based on the statistical field of multidimensional scaling. We show that the use of a squared Euclidean metric (i.e. the SSTRESs measure) gives rise to an annular structure when the input data is drawn from a high-dimensional isotropic distribution, and we provide a theoretical justification for this observation

Connectionist-based intelligent information systems for image analysis and knowledge engineering: applications in horticulture

New Zealand’s main export earnings come from the primary production area including agriculture, horticulture, and viticulture. One of the major contributors in this area of horticulture is the production of quality export grade fruit; specifically apples. In order to maintain a competitive advantage, the systems and methods used to grow the fruit are constantly being refined and are increasingly based on data collected and analysed by both the orchardist who grows the produce and also researchers who refine the methods used to determine high levels of fruit quality. To support the task of data analysis and the resulting decision-making process requires efficient and reliable tools. This thesis attempts to address these issues by applying the techniques of Connectionist-Based Intelligent Information Systems (CBIIS) for Image Analysis and Knowledge Discovery. Using advanced neurocomputing techniques and a novel knowledge engineering methodology, this thesis attempts to seek some solutions to a set of specific problems that exist within the horticultural domain. In particular it describes a methodology based on previous research into neuro-fuzzy systems for knowledge acquisition, manipulation, and extraction and furthers this area by introducing a novel and innovative knowledge-based architecture for knowledge-discovery using an on-line/real-time incremental learning system based on the Evolving Connectionist System (ECOS) paradigm known as the Evolving Fuzzy Neural Network (EFuNN). The emphases of this work highlights knowledge discovery from these data sets using a novel rule insertion and rule extraction method. The advantage of this method is that it can operate on data sets of limited sizes. This method can be used to validate the results produced by the EFuNN and also allow for greater insight into what aspects of the collected data contribute to the development of high quality produce.New Zealand Foundation for Research Science and TechnologyUnpublishedAgrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A. I. (1996). “Fast discovery of association rules” Advances in knowledge discovery and data mining. American Association for Artificial Intelligence Menlo Park, CA, USA pp. 307–328. Aha, D. W., Kilber, D. and Albert, M. K. (1995). “Instance-based learning algorithms” Machine Learning. 6(1): 37–66. Ahrns, I., Bruske, J. and Sommerl, G. (1995). “On-Line Learning with Dynamic Cell Structure” In F. Fogelman-Soulié and P. Gallinari (eds), Proceedings of the 1995 International Conference on Artificial Neural Networks (ICANN’1995). Vol. II EC2 Nanterre, France pp. 141–146. Amari, S. and Kasabov, N. (eds) (1998). Brain-like Computing and Intelligent Information Systems. Springer-Verlag: Singapore. Andrews, R., Deidrich, J. and Tickle, A. B. (1995). “Survey and critique and techniques for extracting rules from trained artificial neural networks” Knowledge-Based Systems. 8(6): 373–389. Andrews, R. and Geva, S. (2002). “Rule extraction from local cluster neural nets” Neurocomputing. 47(1-4): 277–297. Antani, S., Kasturi, R. and Jain, R. (2002). “A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video” Pattern Recognition. 35(4): 945–965. Baesens, B., Setiono, R., Mues, C. and Vanthienen, J. (2003). “Using neural network rule extraction and decision tables as management science tools for credit-risk evaluation” Management Science. 49(3): 312–329. Bakker, B. (2002). “Reinforcement Learning with Long Short-Term Memory” In T. G. Dietterich, S. Becker and Z. Ghahramani (eds), Advances in Neural Information Processing Systems 14. The MIT Press: Cambridge, MA. Baraldi, A. and Blonda, P. (1999). “A survey of fuzzy clustering algorithms for pattern recognition. II” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). 29(6): 786–801. Ben-Hur, A., Horn, D., Siegelmann, H. T. and Vapnik, V. (2001). “A Support Vector Method for Clustering” In T. K. Leen, T. G. Dietterich and V. Tresp (eds), Advances in Neural Information Processing Systems 13. The MIT Press: Cambridge, MA. pp. 367–373. Berkhin, P. (2002). “Survey of Clustering Data Mining Techniques” Technical report. Accrue Software San Jose, CA. Bezdek, J. E. (1993). “Editorial: fuzzy models-what are they, and why?” IEEE Transactions on Fuzzy Systems. 1(1): 1–6. Bilgiç, T. and Türk¸ sen, I. B. (1999). “Measurement of Membership functions: Theoretical and Experimental Work” In D. Dubois and H. Prade (eds), Handbook of Fuzzy Sets and Systems, Vol. 1 Fundamentals of Fuzzy Sets. Kluwer Academic Publishers. pp. 195–202. Billard, A. and Hayes, G. (1999). “DRAMA, a connectionist architecture for control and learning in autonomous robots” Adaptive Behavior Journal. 7(1): 35–64. Birmingham, W. P. and Kim, J. H. (1985). “DAS/Logic: A Rule-based Logic Design Assistant” Proceedings of The Second Conference on Artificial Intelligence Applications. IEEE Computer Society Press. pp. 264–268. Blackmore, J. and Miikkulainen, R. (1993). “Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map” Proceedings of the 1993 IEEE International Conference on Neural Networks (ICNN’93). Vol. I IEEE Service Center. pp. 450–455. Blue, J. L., Candela, G. T., Grother, P. J., Chellappa, R. and Wilson, C. L. (1994). “Evaluation of Pattern Classifiers for Fingerprint and OCR Applications” Pattern Recognition. 27(4): 485–501. Bonarini, A. (1996). “Evolutionary Learning of Fuzzy rules: competition and cooperation” In W. Pedrycz (ed.), Fuzzy Modelling: Paradigms and Practice. Kluwer Academic Press: Norwell, MA. pp. 265–284. Bourke, P. (2001). “Bicubic Interpolation for Image Scaling”. URL: http://astronomy.swin.edu.au/~pbourke/colour/bicubic/ Brigham, E. (1988). Fast Fourier Transform and Its Applications. Prentice Hall: Upper Saddle River, NJ. Bright, K. P., Sherlock, R. A., Lie, J. and Watsney, M. E. (2000). “Development and use of a whole from model for dairying” In S. Halloy and T. Williams (eds), Applied Complexity: From neural nets to managed landscapes. Institute for Crop and Food Research. pp. 382–389. Brown, D., Craw, I. and Lewthwaite, J. (2001). “A SOM based approach to skin detection with application in real time systems” Proceedings of the British Machine Vision Conference. BMVA. pp. 491–500. Buchanan, B. G. and Shortliffe, E. H. (1984). Rule-based Expert Systems. Addison-Wesley: Reading, MA. Buchanan, J. and Bilkey, D. K. (1997). “Transfer of epileptogenesis between perirhinal cortex and amygdala induced by electical kindling” Brain Research. 771: 71–79. Caron, J. R., Jarvenpa, S. L. and Stoddard, D. B. (1994). “Business Reengineering at CIGNA Corporation: Experiences and Lessons Learned From the First Five Years” Management Information Systems Quarterly. 18(3): 233–250. Carpenter, G. A. and Grossberg, S. (1987). “Discovering order in chaos: Stable self-organization of neural recognition codes” Annals of the New York Academy of Sciences. 504: 33–51. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H. and Rosen, D. B. (1992). “Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps” IEEE Transactions on Neural Networks. 3(5): 618–713. Carpenter, G. and Grossberg, S. (1991). Pattern Recognition By Self Organizing Neural Networks. first edn. The MIT Press: Cambridge, MA. Castellano, G., Castiello, C., Fanelli, A. M. and Mencar, C. 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Connectionist-based intelligent information systems for image analysis and knowledge engineering: applications in horticulture

New Zealand’s main export earnings come from the primary production area including agriculture, horticulture, and viticulture. One of the major contributors in this area of horticulture is the production of quality export grade fruit; specifically apples. In order to maintain a competitive advantage, the systems and methods used to grow the fruit are constantly being refined and are increasingly based on data collected and analysed by both the orchardist who grows the produce and also researchers who refine the methods used to determine high levels of fruit quality. To support the task of data analysis and the resulting decision-making process requires efficient and reliable tools. This thesis attempts to address these issues by applying the techniques of Connectionist-Based Intelligent Information Systems (CBIIS) for Image Analysis and Knowledge Discovery. Using advanced neurocomputing techniques and a novel knowledge engineering methodology, this thesis attempts to seek some solutions to a set of specific problems that exist within the horticultural domain. In particular it describes a methodology based on previous research into neuro-fuzzy systems for knowledge acquisition, manipulation, and extraction and furthers this area by introducing a novel and innovative knowledge-based architecture for knowledge-discovery using an on-line/real-time incremental learning system based on the Evolving Connectionist System (ECOS) paradigm known as the Evolving Fuzzy Neural Network (EFuNN). The emphases of this work highlights knowledge discovery from these data sets using a novel rule insertion and rule extraction method. The advantage of this method is that it can operate on data sets of limited sizes. This method can be used to validate the results produced by the EFuNN and also allow for greater insight into what aspects of the collected data contribute to the development of high quality produce.New Zealand Foundation for Research Science and TechnologyUnpublishedAgrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A. I. (1996). “Fast discovery of association rules” Advances in knowledge discovery and data mining. American Association for Artificial Intelligence Menlo Park, CA, USA pp. 307–328. Aha, D. W., Kilber, D. and Albert, M. K. (1995). “Instance-based learning algorithms” Machine Learning. 6(1): 37–66. Ahrns, I., Bruske, J. and Sommerl, G. (1995). “On-Line Learning with Dynamic Cell Structure” In F. Fogelman-Soulié and P. Gallinari (eds), Proceedings of the 1995 International Conference on Artificial Neural Networks (ICANN’1995). Vol. II EC2 Nanterre, France pp. 141–146. Amari, S. and Kasabov, N. (eds) (1998). Brain-like Computing and Intelligent Information Systems. Springer-Verlag: Singapore. Andrews, R., Deidrich, J. and Tickle, A. B. (1995). “Survey and critique and techniques for extracting rules from trained artificial neural networks” Knowledge-Based Systems. 8(6): 373–389. Andrews, R. and Geva, S. (2002). “Rule extraction from local cluster neural nets” Neurocomputing. 47(1-4): 277–297. Antani, S., Kasturi, R. and Jain, R. (2002). “A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video” Pattern Recognition. 35(4): 945–965. Baesens, B., Setiono, R., Mues, C. and Vanthienen, J. (2003). “Using neural network rule extraction and decision tables as management science tools for credit-risk evaluation” Management Science. 49(3): 312–329. Bakker, B. (2002). “Reinforcement Learning with Long Short-Term Memory” In T. G. Dietterich, S. Becker and Z. Ghahramani (eds), Advances in Neural Information Processing Systems 14. The MIT Press: Cambridge, MA. Baraldi, A. and Blonda, P. (1999). “A survey of fuzzy clustering algorithms for pattern recognition. II” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). 29(6): 786–801. Ben-Hur, A., Horn, D., Siegelmann, H. T. and Vapnik, V. (2001). “A Support Vector Method for Clustering” In T. K. Leen, T. G. Dietterich and V. Tresp (eds), Advances in Neural Information Processing Systems 13. The MIT Press: Cambridge, MA. pp. 367–373. Berkhin, P. (2002). “Survey of Clustering Data Mining Techniques” Technical report. Accrue Software San Jose, CA. Bezdek, J. E. (1993). “Editorial: fuzzy models-what are they, and why?” IEEE Transactions on Fuzzy Systems. 1(1): 1–6. Bilgiç, T. and Türk¸ sen, I. B. (1999). “Measurement of Membership functions: Theoretical and Experimental Work” In D. Dubois and H. Prade (eds), Handbook of Fuzzy Sets and Systems, Vol. 1 Fundamentals of Fuzzy Sets. Kluwer Academic Publishers. pp. 195–202. Billard, A. and Hayes, G. 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