Abstract
We compose a toolbox for the design of Minimum Disagreement algorithms. This box contains general procedures which transform (without much loss of efficiency) algorithms that are successful for some d-dimensional (geometric) concept class \(\mathcal{C}\) into algorithms which are successful for a \((d+1)\)-dimensional extension of \(\mathcal{C}\). An iterative application of these transformations has the potential of starting with a base algorithm for a trivial problem and ending up at a smart algorithm for a non-trivial problem. In order to make this working, it is essential that the algorithms are not proper, i.e., they return a hypothesis that is not necessarily a member of \(\mathcal{C}\). However, the “price” for using a super-class \(\mathcal{H}\) of \(\mathcal{C}\) is so low that the resulting time bound for achieving accuracy \(\varepsilon \) in the model of agnostic learning is significantly smaller than the time bounds achieved by the up to date best (proper) algorithms.
We evaluate the transformation technique for \(d=2\) on both artificial and real-life data sets and demonstrate that it provides a fast algorithm, which can successfully solve practical problems on large data sets.
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
Auer, P., Holte, R.C., Maass, W.: Theory and applications of agnostic PAC-learning with small decision trees. In: ICML 1995, pp. 21–29 (1995)
Barbay, J., Chan, T.M., Navarro, G., Pérez-Lantero, P.: Maximum-weight planar boxes in \(O(n^2)\) time (and better). Information Processing Letters 114(8), 437–445 (2014)
de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational Geometry: Algorithms and Applications. Springer-Verlag, Santa Clara (2008)
Bock, R., Chilingarian, A., Gaug, M., Hakl, F., Hengstebeck, T., Jiřina, M., Klaschka, J., Kotrč, E., Savický, P., Towers, S., Vaiciulis, A., Wittek, W.: Methods for multidimensional event classification: a case study using images from a cherenkov gamma-ray telescope. Nuclear Instruments and Methods in Physics Research A 516(2–3), 511–528 (2004)
Cortés, C., Díaz-Báñez, J.M., Pérez-Lantero, P., Seara, C., Urrutia, J., Ventura, I.: Bichromatic separability with two boxes: A general approach. Journal of Algorithms 64(2–3), 79–88 (2009)
Dvořák, J., Savický, P.: Softening splits in decision trees using simulated annealing. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4431, pp. 721–729. Springer, Heidelberg (2007)
Evett, I.W., Spiehler, E.J.: Rule induction in forensic science. Tech. rep, Central Research Establishment, Home Office Forensic Science Service (1987)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11(1), 63–91 (1993)
Kearns, M.J., Schapire, R.E., Sellie, L.M.: Toward efficient agnostic learning. Machine Learning 17(2), 115–141 (1994)
Maass, W.: Efficient agnostic PAC-learning with simple hypothesis. In: COLT 1994, pp. 67–75 (1994)
Pitt, L., Valiant, L.G.: Computational limitations on learning from examples. Journal of the Association on Computing Machinery 35(4), 965–984 (1988)
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press (2014)
Vapnik, V.: Statistical learning theory. Wiley & Sons (1998)
Vapnik, V.N., Chervonenkis, A.Y.: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications XVI(2), 264–280 (1971)
Weiss, S.M., Galen, R.S., Tadepalli, P.: Maximizing the predictive value of production rules. Artificial Intelligence 45(1–2), 47–71 (1990)
Weiss, S.M., Kapouleas, I.: An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In: IJCAI 1989, pp. 781–787 (1989)
Weiss, S.M., Kulikowski, C.A.: Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems. Morgan Kaufmann (1990)
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
Darnstädt, M., Ries, C., Simon, H.U. (2015). Hierarchical Design of Fast Minimum Disagreement Algorithms. In: Chaudhuri, K., GENTILE, C., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2015. Lecture Notes in Computer Science(), vol 9355. Springer, Cham. https://doi.org/10.1007/978-3-319-24486-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-24486-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24485-3
Online ISBN: 978-3-319-24486-0
eBook Packages: Computer ScienceComputer Science (R0)