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ORCIDIn memory of Ray J. Solomonoff 2011
- David L. Dowe:
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. Lecture Notes in Computer Science 7070, Springer 2013, ISBN 978-3-642-44957-4
Introduction
- David L. Dowe:
Introduction to Ray Solomonoff 85th Memorial Conference. 1-36
Invited Papers
- Grace Solomonoff:
Ray Solomonoff and the New Probability. 37-52 - Leonid A. Levin:
Universal Heuristics: How Do Humans Solve "Unsolvable" Problems? 53-54 - Ming Li:
Partial Match Distance - In Memoriam Ray Solomonoff 1926-2009. 55-64
Long Papers
- David Balduzzi
:
Falsification and Future Performance. 65-78 - Douglas Ian Campbell:
The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"? 79-90 - Jukka Corander, Yaqiong Cui, Timo Koski:
Inductive Inference and Partition Exchangeability in Classification. 91-105 - Reginaldo Inojosa da Silva Filho, Ricardo Luis de Azevedo da Rocha, Ricardo Henrique Gracini Guiraldelli:
Learning in the Limit: A Mutational and Adaptive Approach. 106-118 - Jean-Louis Dessalles:
Algorithmic Simplicity and Relevance. 119-130 - T. Mark Ellison:
Categorisation as Topographic Mapping between Uncorrelated Spaces. 131-141 - Rusins Freivalds:
Algorithmic Information Theory and Computational Complexity. 142-154 - Nir Fresco:
A Critical Survey of Some Competing Accounts of Concrete Digital Computation. 155-173 - J. Storrs Hall:
Further Reflections on the Timescale of AI. 174-183 - Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh:
Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. 184-197 - Norbert Jankowski:
Complexity Measures for Meta-learning and Their Optimality. 198-210 - Allen King:
Design of a Conscious Machine. 211-222 - Tor Lattimore, Marcus Hutter:
No Free Lunch versus Occam's Razor in Supervised Learning. 223-235 - Shane Legg, Joel Veness:
An Approximation of the Universal Intelligence Measure. 236-249 - Enes Makalic, Daniel F. Schmidt:
Minimum Message Length Analysis of the Behrens-Fisher Problem. 250-260 - Enes Makalic, Lloyd Allison:
MMLD Inference of Multilayer Perceptrons. 261-272 - Kenshi Miyabe:
An Optimal Superfarthingale and Its Convergence over a Computable Topological Space. 273-284 - Eray Özkural:
Diverse Consequences of Algorithmic Probability. 285-298 - Kristiaan Pelckmans:
An Adaptive Compression Algorithm in a Deterministic World. 299-305 - Steve Petersen:
Toward an Algorithmic Metaphysics. 306-317 - Rafal Rzepka, Koichi Muramoto, Kenji Araki:
Limiting Context by Using the Web to Minimize Conceptual Jump Size. 318-326 - Daniel F. Schmidt:
Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models. 327-338 - Adrian Silvescu, Vasant G. Honavar:
Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction. 339-350 - Alex Solomonoff:
Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation. 351-365 - Ray J. Solomonoff, Elias G. Saleeby:
On the Application of Algorithmic Probability to Autoregressive Models. 366-385 - Peter Sunehag, Marcus Hutter:
Principles of Solomonoff Induction and AIXI. 386-398 - Joe Suzuki:
MDL/Bayesian Criteria Based on Universal Coding/Measure. 399-410 - Hayato Takahashi:
Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers. 411-416 - Ian Wood, Peter Sunehag, Marcus Hutter:
(Non-)Equivalence of Universal Priors. 417-425 - John Robert Woodward, Jerry Swan:
A Syntactic Approach to Prediction. 426-438
Short Paper
- Amiza Amir, Anang Hudaya Muhamad Amin, Asad I. Khan:
Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory. 439-443
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