Algorithmic Learning Theory: 22nd International Conference, by Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas

By Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (auth.), Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (eds.)

This e-book constitutes the refereed complaints of the twenty second foreign convention on Algorithmic studying idea, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th overseas convention on Discovery technology, DS 2011.
The 28 revised complete papers offered including the abstracts of five invited talks have been conscientiously reviewed and chosen from a variety of submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit difficulties, on-line studying, kernel and margin-based equipment, clever brokers and different studying models.

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Extra info for Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings

Example text

6926. J. Kivinen et al. ): ALT 2011, LNAI 6925, p. 39, 2011. de Abstract. We analyze iterative learning in the limit from positive data with the additional information provided by a counter. The simplest type of counter provides the current iteration number (counting up from 0 to infinity), which is known to improve learning power over plain iterative learning. We introduce five other (weaker) counter types, for example only providing some unbounded and non-decreasing sequence of numbers. Analyzing these types allows for understanding what properties of a counter can benefit learning.

On the Expressive Power of Deep Architectures 31 To prevent a trivial solution in which the encoder weights go to zero and the decoder weights to infinity, the contractive autoencoder uses tied weights (the decoder weights are forced to be the transpose of the encoder weights). Because of the contractive criterion, what we find empirically is that for any particular input example, many of the hidden units saturate while a few remain sensitive to changes in the input (corresponding to changes in the directions of changes expected under the data distribution).

Leen, T. ) Advances in Neural Information Processing Systems (NIPS 1994), vol. 7, pp. 657–664. : Learning a similarity metric discriminatively, with application to face verification. In: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR 2005). : A unified architecture for natural language processing: Deep neural networks with multitask learning. T. ) Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML 2008), pp. 160–167. : Tempered Markov chain monte carlo for training of restricted Boltzmann machine.

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