Abstract
We study in a quantitative way the efficacy of a social intelligence scheme that is an extension of Extreme Learning Machine paradigm. The key question we investigate is whether and how a collection of elementary learning parcels can replace a single algorithm that is well suited to learn a relatively complex function. Per se, the question is definitely not new, as it can be met in various fields ranging from social networks to bio-informatics. We use a well known benchmark as a touchstone to contribute its answer with both theoretical and numerical considerations.
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References
Apolloni, B., Kurfess, F. (eds.): From Synapses to Rules—Discovering Symbolic Rules from Neural Processed Data. Kluwer Academic/Plenum Publishers, New York (2002)
Dietterich T.G.: Ensemble methods in machine learning. In: Multiple Classifier Systems, pp. 1–15. Springer Berlin Heidelberg, Berlin, Heidelberg (2000)
Apolloni, B., Malchiodi, D., Taylor, J.: Learning by gossip: a principled information exchange model in social networks. Cogn. Comput. 5, 327–339 (2013). https://doi.org/10.1007/s12559-013-9211-6
Huang, G.-B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16), 3056–3062 (2007)
Lukoševičius, M., Jaeger, H.: Survey: reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)
Lukoševičius, M.: A Practical Guide to Applying Echo State Networks, pp. 659–686. Springer, Berlin Heidelberg, Berlin, Heidelberg (2012)
Zhang, Y., Li, P., Jin, Y., Choe, Y.: A digital liquid state machine with biologically inspired learning and its application to speech recognition. IEEE Trans. Neural Networks Learn. Syst. 26(11), 2635–2649 (2015)
Wilks, S.S.: Mathematical Statistics, Wiley Publications in Statistics. Wiley, New York (1962)
Hannig, J.: On generalized fiducial inference. Statistica Sinica 19(2), 491–544 (2009)
Iyer, H.K., Patterson, P.: A recipe for constructing generalized pivotal quantities and generalized confidence intervals, Tech. Rep. 2e002/10, Department of Statistics, Colorado State University (2002)
Martin, R., Liu, C.: Inferential models: a framework for prior-free posterior probabilistic inference. J. Am. Stat. Assoc. 108(501), 301–313 (2013)
Apolloni, B., Pedrycz, W., Bassis, S., Malchiodi, D.: The Puzzle of Granular Computing. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-79864-4
Apolloni, B., Bassis, S., Malchiodi, D.: Compatible worlds. Nonlinear Anal.: Theory, Methods Appl. 71(12), e2883–e2901 (2009)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Apolloni, B., Malchiodi, D.: Gaining degrees of freedom in subsymbolic learning. Theoret. Comput. Sci. 255, 295–321 (2001)
Abu-Mostafa, Y.S.: Hints and the vc dimension. Neural Comput. 5(2), 278–288 (1993)
Baum, E.B., Haussler, D.: What size net gives valid generalization? Neural Comput. 1(1), 151–160 (1989)
Apolloni, B., Malchiodi, D., Gaito, S.: Algorithmic Inference in Machine Learning, vol. 5. Advanced Knowledge International Pty, ADELAIDE—AUS (2006)
Corke, P.I.: A robotics toolbox for matlab. IEEE Robot. Autom. Mag. 3(1), 24–32 (1996)
Apolloni, B., Bassis, S., Valerio, L.: Training a network of mobile neurons. In: The 2011 International Joint Conference on Neural Networks, pp. 1683–1691 (2011)
Rasmussen, C.E., Neal, R.M., Hinton, G.E., van Camp, D., Revow, M., Ghahramani, Z., Kustra, R., Tibshirani, R.J.: The delve manual (1996). http://www.cs.toronto.edu/~delve/
Cesa-Bianchi, N., Mansour, Y., Shamir, O.: On the complexity of learning with kernels, CoRR abs/1411.1158. http://arxiv.org/abs/1411.1158
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Apolloni, B., Shehhi, A.A., Damiani, E. (2021). The Simplification Conspiracy. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_2
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