Abstract
Machine learning methods and algorithms can be combined into ensembles to obtain better performance than a single base learner. In the paper we present a framework for distributed system based on Common Object Request Broker Architecture for creating ensembles of learning systems. The systems are handled by the server which sends and receives learning and testing data. They can be located on different machines with various operating systems or hardware. The structures of the base learners are described by XML files.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akhtar, Z., Rattani, A., Foresti, G.L.: Temporal analysis of adaptive face recognition. Journal of Artificial Intelligence and Soft Computing Research 4(4), 243–255 (2014)
Boukhtouta, A., Berger, J., George, A., Powell, W.B.: An approximate dynamic programming approach for semi-cooperative multi-agent resource management. Journal of Artificial Intelligence and Soft Computing Research 2(3), 201–214 (2012)
Breiman, L.: Bias, variance, and arcing classifiers. Technical Report In: Technical Report 460, Statistics Department, University of California (1997)
Bruzdzinski, T., Krzyzak, A., Fevens, T., Jelen, Ł.: Web–based framework for breast cancer classification. Journal of Artificial Intelligence and Soft Computing Research 4(2), 149–162 (2014)
Chu, J.L., Krzyak, A.: The recognition of partially occluded objects with support vector machines and convolutional neural networks and deep belief networks. Journal of Artificial Intelligence and Soft Computing Research 4(1), 5–19 (2014)
Collobert, R., Bengio, S., Mariéthoz, J.: Torch: a modular machine learning software library. Technical report, IDIAP (2002)
Cpalka, K., Rutkowski, L.: Flexible takagi-sugeno fuzzy systems. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 3, pp. 1764–1769 (July 2005)
Folly, K.A.: Parallel pbil applied to power system controller design. Journal of Artificial Intelligence and Soft Computing Research 3(3), 215–223 (2013)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Kilicaslan, Y., Tuna, G.: An nlp-based approach for improving human-robot interaction. Journal of Artificial Intelligence and Soft Computing Research 3(3), 189–200 (2013)
Korytkowski, M., Scherer, R., Rutkowski, L., Drozda, G.: Xml-based language for connectionist structure description. In: Cader, A., Rutkowski, L., Tadeusiewicz, R., Zurada, J. (eds.) Artificial Intelligence and Soft Computing, pp. 469–474. Academic Publishing House EXIT, Warsaw (2006)
Koshiyama, A.S., Vellasco, M.M.B.R., Tanscheit, R.: Gpfis-control: A genetic fuzzy system for control tasks. Journal of Artificial Intelligence and Soft Computing Research 4(3), 167–179 (2014)
Kuncheva, L.: Combining Pattern Classifiers. STUDFUZZ. John Wiley & Sons (2004)
Lichman, M.: UCI machine learning repository (2013)
Meir, R., Rätsch, G.: An introduction to boosting and leveraging. In: Mendelson, S., Smola, A.J. (eds.) Advanced Lectures on Machine Learning. LNCS (LNAI), vol. 2600, pp. 118–183. Springer, Heidelberg (2003)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011)
Rigatos, G.G., Siano, P.: Flatness-based adaptive fuzzy control of spark-ignited engines. Journal of Artificial Intelligence and Soft Computing Research 4(4), 231–242 (2014)
Rutkowski, L.: Flexible Neuro-Fuzzy Systems. Kluwer Academic Publishers (2004)
Rutkowski, L.: Computational Intelligence Methods and Techniques. Springer, Heidelberg (2008)
Saitoh, D., Hara, K.: Mutual learning using nonlinear perceptron. Journal of Artificial Intelligence and Soft Computing Research 5(1), 71–77 (2015)
Schapire, R.E.: A brief introduction to boosting. In: Conference on Artificial Intelligence, pp. 1401–1406 (1999)
Setness, M., Babuska, R.: Bagging predictors. Machine Learning 26(2), 123–140 (1996)
Tambouratzis, T., Chernikova, D., Pazsit, I.: Pulse shape discrimination of neutrons and gamma rays using kohonen artificial neural networks. Journal of Artificial Intelligence and Soft Computing Research 3(2), 77–88 (2013)
Theodoridis, D.C., Boutalis, Y.S., Christodoulou, M.A.: Robustifying analysis of the direct adaptive control of unknown multivariable nonlinear systems based on a new neuro-fuzzy method. Journal of Artificial Intelligence and Soft Computing Research 1(1), 59–79 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Korytkowski, M., Scherer, M., Ferdowsi, S. (2015). Software Framework for Modular Machine Learning Systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-19369-4_67
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19368-7
Online ISBN: 978-3-319-19369-4
eBook Packages: Computer ScienceComputer Science (R0)