Algorithmic Learning Theory: 22nd International Conference, - download pdf or read online

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

ISBN-10: 3642244114

ISBN-13: 9783642244117

ISBN-10: 3642244122

ISBN-13: 9783642244124

This e-book constitutes the refereed complaints of the twenty second foreign convention on Algorithmic studying conception, 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 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 equipment, clever brokers and different studying models.

Show description

Read or Download Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings PDF

Best international books

Download PDF by C.C. Huang, J.Y. Yang (auth.), Professor Dr. Kozo Fujii,: New Developments in Computational Fluid Dynamics:

This quantity comprises 20 papers awarded on the 6th foreign Nobeyama Workshop at the New Century of Computational Fluid Dynamics, Nobeyama, Japan, April 21-24, 2003. The Nobeyama Workshop makes a speciality of predicting the subsequent 100 years of improvement of Fluid Dynamics, accounting for the present prestige and destiny traits of excessive functionality computation and verbal exchange.

Progress in Precision Engineering: Proceedings of the 6th - download pdf or read online

By way of Professor Pat McKeown Cranfield Precision Engineering, united kingdom Member of Joint establishing Committee IPES6/UME2 development IN PRECISION ENGINEERING steel operating businesses in device making, prototype manu­ facture and subcontract machining frequently use the label "precision engineering" to point that they're conversant in operating to finer tolerances than is generally anticipated in sequence construction.

Bernhard K. Aichernig, Elisabeth Jöbstl, Matthias Kegele's Tests and Proofs: 7th International Conference, TAP 2013, PDF

This ebook constitutes the refereed complaints of the seventh foreign convention on attempt and Proofs, faucet 2013, held in Budapest, Hungary, in June 2013, as a part of the STAF 2013 Federated meetings. The 12 revised complete papers offered including one instructional have been rigorously reviewed and chosen from 24 submissions.

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

Example text

Even though a Boltzmann Machine is a parametric model when we consider the dimensionality nh of h to be fixed, in practice one allows nh to vary, making it a non-parametric model. With nh large enough, one can model any discrete distribution: Le Roux and Bengio (2008) showed that Restricted Boltzmann Machines (described below) are universal approximators, and since they are special cases 28 Y. Bengio and O. Delalleau of Boltzmann Machines, Boltzmann Machines also are universal approximators.

Show that the number of unique √ products found in the expanded polynomial representation of f ∈ Fn is 2 n−1 . 2. Prove that the only possible architecture for a shallow sum-product network to compute f is to have a hidden layer made of product units, with a sum unit as output. 3. Conclude that the number of hidden units in step 2 must be at least the number of unique products computed in step 1. For family G, we obtain that a shallow sum-product network computing gin ∈ Gin must have at least (n−1)i hidden units.

RBMs are typically trained by stochastic gradient descent, using a noisy (and generally biased) estimator of the above log-likelihood gradient. , 2006), and it has a particularly simple form: the negative phase gradient is obtained by starting a very short chain (usually just one step) at the observed x and replacing the above expectations by the corresponding samples. , 2010) or unsupervised (Hinton and Salakhutdinov, 2006) neural network. Another common way to train RBMs is based on the Stochastic Maximum Likelihood (SML) estimator (Younes, 1999) of the gradient, also called Persistent Contrastive Divergence (PCD; Tieleman, 2008) when it was introduced for RBMs.

Download PDF sample

Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings by Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (auth.), Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann (eds.)


by Jeff
4.2

Rated 4.51 of 5 – based on 28 votes