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Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 -- December 2, 2011 [Pehme köide]

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  • Formaat: Paperback / softback, 445 pages, kõrgus x laius: 235x155 mm, kaal: 706 g, 61 Illustrations, black and white; XVI, 445 p. 61 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 7070
  • Ilmumisaeg: 11-Nov-2013
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642449573
  • ISBN-13: 9783642449574
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  • Formaat: Paperback / softback, 445 pages, kõrgus x laius: 235x155 mm, kaal: 706 g, 61 Illustrations, black and white; XVI, 445 p. 61 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 7070
  • Ilmumisaeg: 11-Nov-2013
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642449573
  • ISBN-13: 9783642449574
Teised raamatud teemal:
Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines (UTMs) could be used for universal Bayesian prediction and artificial intelligence (machine learning). This work continues to increasingly influence and under-pin statistics, econometrics, machine learning, data mining, inductive inference, search algorithms, data compression, theories of (general) intelligence and philosophy of science - and applications of these areas. Ray not only envisioned this as the path to genuine artificial intelligence, but also, still in the 1960s, anticipated stages of progress in machine intelligence which would ultimately lead to machines surpassing human intelligence. Ray warned of the need to anticipate and discuss the potential consequences - and dangers - sooner rather than later. Possibly foremostly, Ray Solomonoff was a fine, happy, frugal and adventurous human being of gentle resolve who managed to fund himself while electing to conduct so much of his paradigm-changing research outside of the university system. The volume contains 35 papers pertaining to the abovementioned topics in tribute to Ray Solomonoff and his legacy.
Introduction
Introduction to Ray Solomonoff 85th Memorial Conference 1(36)
David L. Dowe
Invited Papers
Ray Solomonoff and the New Probability
37(16)
Grace Solomonoff
Universal Heuristics: How Do Humans Solve "Unsolvable" Problems?
53(2)
Leonid A. Levin
Partial Match Distance
55(10)
Ming Li
Long Papers
Falsification and Future Performance
65(14)
David Balduzzi
The Semimeasure Property of Algorithmic Probability -- "Feature" or "Bug"?
79(12)
Douglas Campbell
Inductive Inference and Partition Exchangeability in Classification
91(15)
Jukka Corander
Yaqiong Cui
Timo Koski
Learning in the Limit: A Mutational and Adaptive Approach
106(13)
Reginaldo Inojosa da Silva Filho
Ricardo Luis de Azevedo da Rocha
Ricardo Henrique Gracini Guiraldelli
Algorithmic Simplicity and Relevance
119(12)
Jean-Louis Dessalles
Categorisation as Topographic Mapping between Uncorrelated Spaces
131(11)
T. Mark Ellison
Algorithmic Information Theory and Computational Complexity
142(13)
Rusins Freivalds
A Critical Survey of Some Competing Accounts of Concrete Digital Computation
155(19)
Nir Fresco
Further Reflections on the Timescale of AI
174(10)
J. Storrs Hall
Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL
184(14)
Bing Hu
Thanawin Rakthanmanon
Yuan Hao
Scott Evans
Stefano Lonardi
Eamonn Keogh
Complexity Measures for Meta-learning and Their Optimality
198(13)
Norbert Jankowski
Design of a Conscious Machine
211(12)
P. Allen King
No Free Lunch versus Occam's Razor in Supervised Learning
223(13)
Tor Lattimore
Marcus Hutter
An Approximation of the Universal Intelligence Measure
236(14)
Shane Legg
Joel Veness
Minimum Message Length Analysis of the Behrens-Fisher Problem
250(11)
Enes Makalic
Daniel F. Schmidt
MMLD Inference of Multilayer Perceptrons
261(12)
Enes Makalic
Lloyd Allison
An Optimal Superfarthingale and Its Convergence over a Computable Topological Space
273(12)
Kenshi Miyabe
Diverse Consequences of Algorithmic Probability
285(14)
Eray Ozkural
An Adaptive Compression Algorithm in a Deterministic World
299(7)
Kristiaan Pelckmans
Toward an Algorithmic Metaphysics
306(12)
Steve Petersen
Limiting Context by Using the Web to Minimize Conceptual Jump Size
318(9)
Rafal Rzepka
Koichi Muramoto
Kenji Araki
Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models
327(12)
Daniel F. Schmidt
Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction
339(12)
Adrian Silvescu
Vasant Honavar
Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation
351(15)
Alex Solomonoff
On the Application of Algorithmic Probability to Autoregressive Models
366(20)
Ray J. Solomonoff
Elias G. Saleeby
Principles of Solomonoff Induction and AIXI
386(13)
Peter Sunehag
Marcus Hutter
MDL/Bayesian Criteria Based on Universal Coding/Measure
399(12)
Joe Suzuki
Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers
411(6)
Hayato Takahashi
(Non-)Equivalence of Universal Priors
417(9)
Ian Wood
Peter Sunehag
Marcus Hutter
A Syntactic Approach to Prediction
426(13)
John Woodward
Jerry Swan
Short Paper
Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory
439(6)
Amiza Amir
Anang Hudaya M. Amin
Asad Khan
Author Index 445