A new oscillating-error technique for classifiers

References

• Asim, A., Li, Y., Xie, Y., & Zhu, Y. (2002). Data mining for abalone, computer science 4TF3 project. Supervised by Dr Jiming Peng. Hamilton: Department of Computing and Software, McMaster University.
   Google Scholar
• Brattka, V. (2003). A computable Kolmogorov superposition theorem. Computability and Complexity in Analysis. Informatik Berichte, 272, 7–22.
   Google Scholar
• Carpenter, G., Grossberg, S., & Rosen, D. (1991). Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4, 759–771.10.1016/0893-6080(91)90056-B
   Web of Science ®Google Scholar
• Chen, S., Cai, D., Pearce, K., Sun, P.Y-W., Roberts, A. C., & Glanzman, D. L. (2014). Reinstatement of long-term memory following erasure of its behavioral and synaptic expression in Aplysia. eLife 2014, 3, 1–21. doi:10.7554/eLife.03896
   Google Scholar
• Dershowitz, N., & Falkovich, E. (2015). Cellular automata are generic. In U. Dal Lago & R. Harmer (Eds.), Developments in Computational Models 2014 (DCM 2014) EPTCS (Vol. 179, pp. 17–32). doi:10.4204/EPTCS.179.2
   Google Scholar
• Farmer, D., Toffoli, T., & Wolfram, S. (Eds.). (1984). Cellular automata: Proceedings of an interdisciplinary workshop, Los Alamos, New Mexico 87545, USA, March 7–11, 1983 (Vol. 10). North Holland.
   Google Scholar
• Fisher, R. A. (1950/1936). The use of multiple measurements in taxonomic problems, Annual Eugenics 7, Part II. In Contributions to mathematical statistics (pp. 179–188). New York, NY: John Wiley.
   Google Scholar
• Forina, M., Leardi, R., Armanino, C., & Lanteri, S. (1991). PARVUS - An extendible package for data exploration, classification and correlation. Genoa: Institute of Pharmaceutical and Food Analysis and Technologies.
   Google Scholar
• Frey, P. W., & Slate, D. J. (1991). Letter recognition using Holland-style adaptive classifiers. Machine learning, 6, 161–182.
   Web of Science ®Google Scholar
• Friedman, J. H. (1989). Regularized discriminant analysis. Journal of the American Statistical Association, 84, 165–175.10.1080/01621459.1989.10478752
   Web of Science ®Google Scholar
• Gallant, S. I. (1990). Perceptron-based learning algorithms. IEEE Transactions on Neural Networks, 1, 179–191.
   Google Scholar
• Garcke, J., & Griebel, M. (2002). Classification with sparse grids using simplicial basis functions. Intelligent data analysis, 6, 483–502.
   Google Scholar
• Greer, K. (2013). Artificial neuron modelling based on wave shape, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 4, 20–25. ISSN 2067-3957 (online), ISSN 2068-0473 (print).
   Google Scholar
• Greer, K. (2015). A single-pass classifier for categorical data. Retrieved from arXiv website http://arxiv.org/abs/1503.02521
   Google Scholar
• Grossberg, S. (2013). Adaptive resonance theory. Scholarpedia, 8, 1569–.10.4249/scholarpedia.1569
   Google Scholar
• Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5, 989–993.10.1109/72.329697
   PubMed Web of Science ®Google Scholar
• Hayes-Roth, B., & Hayes-Roth, F. (1977). Concept learning and the recognition and classification of exemplars. Journal of Verbal Learning and Verbal Behavior, 16, 321–338.10.1016/S0022-5371(77)80054-6
   Google Scholar
• Hect-Nielsen, R. (1990). Neurocomputing. Reading, MA: Addison-Wesley.
   Google Scholar
• Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554.10.1162/neco.2006.18.7.1527
   PubMed Web of Science ®Google Scholar
• Hoffman, J., Tzeng, E., Donahue, J., Jia, Y., Saenko, K., & Darrell, T. (2014). One-shot adaptation of supervised deep convolutional models. Retrieved from arXiv 1312.6204v2 [cs.CV].
   Google Scholar
• Jiang, Y., & Zhou, Z.-H. (2004). Editing training data for knn classifiers with neural network ensemble. In Lecture Notes in Computer Science, 3173, 356–361.10.1007/b99834
   Google Scholar
• Kahraman, H. T., Sagiroglu, S., & Colak, I. (2013). The development of intuitive knowledge classifier and the modeling of domain dependent data. Knowledge-Based Systems, 37, 283–295.10.1016/j.knosys.2012.08.009
   Web of Science ®Google Scholar
• Kolmogorov, A. N. (1963). On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition. American Mathematical Society Translation, 28, 55–59.10.1090/trans2/028
   Google Scholar
• Kurgan, L. A., Cios, K. J., Tadeusiewicz, R., Ogiela, M., & Goodenday, L. S. (2001). Knowledge discovery approach to automated cardiac SPECT diagnosis. Artificial Intelligence in Medicine, 23, 149–169.10.1016/S0933-3657(01)00082-3
   PubMed Web of Science ®Google Scholar
• Liver dataset. (2016). Forsyth, R. S. (1990). BUPA Medical Research Ltd. Retrieved from https://archive.ics.uci.edu/ml/datasets/Liver+Disorders
   Google Scholar
• Lohweg, V., Dörksen, H., Hoffmann, J. L., Hildebrand, R., Gillich, E., Schaede, J., & Hofmann, J. (2013). Banknote authentication with mobile devices. In IS&T/SPIE electronic imaging (pp. 866507–866507). International Society for Optics and Photonics.
   Google Scholar
• Pershin, Y. V., La Fontaine, S., & Di Ventra, M. (2008). Memristive model of amoeba’s learning. Retrieved October 22, 2008, from E-print arXiv 0810.4179
   Google Scholar
• Rojas, R. (1996). Neural networks: A systematic introduction. Berlin: Springer-Verlag. [online] Retrieved from books.google.com.10.1007/978-3-642-61068-4
   Google Scholar
• UCI Machine Learning Repository. (2016). Retrieved from http://archive.ics.uci.edu/ml/
   Google Scholar
• Waxman, S. G. (2012). Sodium channels, the electrogenisome and the electrogenistat: Lessons and questions from the clinic. The Journal of Physiology, 590, 2601–2612.10.1113/jphysiol.2012.228460
   PubMed Web of Science ®Google Scholar
• Weka. (2015). Retrieved from http://www.cs.waikato.ac.nz/ml/weka/index.html
   Google Scholar
• Widrow, B., & Lehr, M. (1990). 30 years of adaptive neural networks: Perceptron, Madaline, and backpropagation. Proceedings of the IEEE, 78, 1415–1442.10.1109/5.58323
   Web of Science ®Google Scholar
• Zoo database. (2016). Forsyth, R.S. BUPA Medical Research Ltd. Retrieved from https://archive.ics.uci.edu/ml/datasets/Zoo
   Google Scholar