By Marcus Hutter
This quantity includes the papers awarded on the 18th foreign Conf- ence on Algorithmic studying conception (ALT 2007), which used to be held in Sendai (Japan) in the course of October 1–4, 2007. the most target of the convention was once to supply an interdisciplinary discussion board for fine quality talks with a robust theore- cal history and scienti?c interchange in parts reminiscent of question versions, online studying, inductive inference, algorithmic forecasting, boosting, aid vector machines, kernel tools, complexity and studying, reinforcement studying, - supervised studying and grammatical inference. The convention was once co-located with the 10th foreign convention on Discovery technological know-how (DS 2007). This quantity contains 25 technical contributions that have been chosen from 50 submissions by means of the ProgramCommittee. It additionally comprises descriptions of the ?ve invited talks of ALT and DS; longer models of the DS papers are available the court cases of DS 2007. those invited talks have been provided to the viewers of either meetings in joint sessions.
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Additional resources for Algorithmic Learning Theory: 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007. Proceedings
Local learning algorithms. : Independent component analysis, a new concept? : A unifying framework for independent component analysis. Comput. Math. Appl. : Kernel independent component analysis. J. Mach. Learn. Res. : Measuring statistical dependence with Hilbert-Schmidt norms. , Tomita, E. ) Proceedings Algorithmic Learning Theory, pp. 63–77. : Kernel methods for measuring independence. J. Mach. Learn. Res. : Fast kernel ICA using an approximate newton method. : A consistent test for bivariate dependence.
Proc. Intl. Conf. : Bhattacharyya and expected likelihood kernels. K. ) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 57–71. Springer, Heidelberg (2003) Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity J¨ urgen Schmidhuber TU Munich, Boltzmannstr. ch/~juergen I postulate that human or other intelligent agents function or should function as follows. ’ At any time, given some agent’s current coding capabilities, part of the data is compressible by a short and hopefully fast program / description / explanation / world model.
A. Servedio, and E. ): ALT 2007, LNAI 4754, pp. 34–48, 2007. c Springer-Verlag Berlin Heidelberg 2007 Feasible Iteration of Feasible Learning Functionals 1 35 Introduction and Motivation One-shot (algorithmic) learners, on input data about a function, output at most a single (hopefully correct) conjectured program [JORS99]. Feasible (deterministic) one-shot function learning can be modeled by the polytime multi-tape Oracle Turing machines (OTMs) as used in [IKR01] (see also [KC96, Meh76]). We call the corresponding functionals basic feasible functionals.