By Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (auth.), Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (eds.)
This ebook constitutes the refereed lawsuits of the twenty second foreign convention on Algorithmic studying idea, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th foreign convention on Discovery technological know-how, DS 2011.
The 28 revised complete papers offered including the abstracts of five invited talks have been rigorously reviewed and chosen from quite a few submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit difficulties, on-line studying, kernel and margin-based tools, clever brokers and different studying models.
Read or Download Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings PDF
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Additional resources for Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings
Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, pp. 1–12. : Connectionist learning procedures. Artiﬁcial Intelligence 40, 185–234 (1989) 34 Y. Bengio and O. : Products of experts. In: Proceedings of the Ninth International Conference on Artiﬁcial Neural Networks (ICANN), vol. 1, pp. 1–6. : Reducing the dimensionality of data with neural networks. : Autoencoders, minimum description length, and helmholtz free energy.
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. In: Proceedings of the Thirteenth International Conference on Artiﬁcial Intelligence and Statistics (AISTATS 2010), pp. : Why does unsupervised pre-training help deep learning?
Trivially, each single, describable language L has a suitable constant function as an Ex-learner (this learner constantly outputs a description for L). Thus, we are interested for which classes of languages L is there a single learner h learning each member of L. This framework is known as language learning in the limit and has been studied extensively, using a wide range of learning criteria similar to TxtEx-learning (see, for example, the text book [JORS99]). In this paper we are concerned with a memory limited variant of TxtExlearning, namely iterative learning [Wie76, LZ96] (It).