Coverart for item
The Resource 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, David L. Dowe (eds.), (electronic book)

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, David L. Dowe (eds.), (electronic book)

Label
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
Title
Algorithmic probability and friends
Title remainder
Bayesian prediction and artificial intelligence : Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30-December 2, 2011
Statement of responsibility
David L. Dowe (eds.)
Contributor
Editor of compilation
Honoree
Subject
Genre
Language
eng
Summary
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
Member of
Cataloging source
GW5XE
Dewey number
004
Illustrations
illustrations
Index
index present
LC call number
QA75.5
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
2011
http://library.link/vocab/relatedWorkOrContributorName
  • Dowe, David L.
  • Solomonoff, Ray
  • Ray Solomonoff 85th Memorial Conference
Series statement
  • Lecture Notes in artificial intelligence,
  • LNCS sublibrary. SL 1, Theoretical computer science and general issues
Series volume
7070
http://library.link/vocab/subjectName
  • Computer science
  • Artificial intelligence
  • Machine learning
Label
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, David L. Dowe (eds.), (electronic book)
Instantiates
Publication
Note
Includes author index
Antecedent source
unknown
Color
multicolored
Contents
  • Partial Match Distance
  • Ming Li
  • Long Papers.
  • Falsification and Future Performance
  • David Balduzzi
  • The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"?
  • Douglas Campbell
  • Inductive Inference and Partition Exchangeability in Classification
  • Jukka Corander, Yaqiong Cui and Timo Koski
  • Learning in the Limit: A Mutational and Adaptive Approach
  • Introduction.
  • Reginaldo Inojosa da Silva Filho and Ricardo Luis de Azevedo da Rocha
  • Algorithmic Simplicity and Relevance
  • Jean-Louis Dessalles
  • Categorisation as Topographic Mapping between Uncorrelated Spaces
  • T. Mark Ellison
  • Algorithmic Information Theory and Computational Complexity
  • Rūsiņš Freivalds
  • A Critical Survey of Some Competing Accounts of Concrete Digital Computation
  • Nir Fresco
  • Further Reflections on the Timescale of AI
  • Introduction to Ray Solomonoff 85th Memorial Conference
  • J. Storrs Hall
  • Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL
  • Bing Hu ... [et al.]
  • Complexity Measures for Meta-learning and Their Optimality
  • Norbert Jankowski
  • Design of a Conscious Machine
  • P. Allen King
  • No Free Lunch versus Occam's Razor in Supervised Learning
  • Tor Lattimore and Marcus Hutter
  • An Approximation of the Universal Intelligence Measure
  • David L. Dowe
  • Shane Legg and Joel Veness
  • Minimum Message Length Analysis of the Behrens-Fisher Problem
  • Enes Makalic and Daniel F. Schmidt
  • MMLD Inference of Multilayer Perceptrons
  • Enes Makalic and Lloyd Allison
  • An Optimal Superfarthingale and Its Convergence over a Computable Topological Space
  • Kenshi Miyabe
  • Diverse Consequences of Algorithmic Probability
  • Eray Özkural
  • An Adaptive Compression Algorithm in a Deterministic World
  • Invited Papers.
  • Kristiaan Pelckmans
  • Toward an Algorithmic Metaphysics
  • Steve Petersen
  • Limiting Context by Using the Web to Minimize Conceptual Jump Size
  • Rafal Rzepka, Koichi Muramoto and Kenji Araki
  • Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models
  • Daniel F. Schmidt
  • Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction
  • Adrian Silvescu and Vasant Honavar
  • Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation
  • Ray Solomonoff and the New Probability
  • Alex Solomonoff
  • On the Application of Algorithmic Probability to Autoregressive Models
  • Ray J. Solomonoff and Elias G. Saleeby
  • Principles of Solomonoff Induction and AIXI
  • Peter Sunehag and Marcus Hutter
  • MDL/Bayesian Criteria Based on Universal Coding/Measure
  • Joe Suzuki
  • Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers
  • Hayato Takahashi
  • (Non-)Equivalence of Universal Priors
  • Grace Solomonoff
  • Ian Wood, Peter Sunehag and Marcus Hutter
  • A Syntactic Approach to Prediction
  • John Woodward and Jerry Swan
  • Short Paper.
  • Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory
  • Amiza Amir, Anang Hudaya M. Amin and Asad Khan
  • Universal Heuristics: How Do Humans Solve "Unsolvable" Problems?
  • Leonid A. Levin
Control code
SPR864821543
Dimensions
unknown
Extent
1 online resource (xvi, 445 pages)
File format
unknown
Form of item
online
Isbn
9783642449574
Level of compression
unknown
Other control number
10.1007/978-3-642-44958-1
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
Label
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, David L. Dowe (eds.), (electronic book)
Publication
Note
Includes author index
Antecedent source
unknown
Color
multicolored
Contents
  • Partial Match Distance
  • Ming Li
  • Long Papers.
  • Falsification and Future Performance
  • David Balduzzi
  • The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"?
  • Douglas Campbell
  • Inductive Inference and Partition Exchangeability in Classification
  • Jukka Corander, Yaqiong Cui and Timo Koski
  • Learning in the Limit: A Mutational and Adaptive Approach
  • Introduction.
  • Reginaldo Inojosa da Silva Filho and Ricardo Luis de Azevedo da Rocha
  • Algorithmic Simplicity and Relevance
  • Jean-Louis Dessalles
  • Categorisation as Topographic Mapping between Uncorrelated Spaces
  • T. Mark Ellison
  • Algorithmic Information Theory and Computational Complexity
  • Rūsiņš Freivalds
  • A Critical Survey of Some Competing Accounts of Concrete Digital Computation
  • Nir Fresco
  • Further Reflections on the Timescale of AI
  • Introduction to Ray Solomonoff 85th Memorial Conference
  • J. Storrs Hall
  • Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL
  • Bing Hu ... [et al.]
  • Complexity Measures for Meta-learning and Their Optimality
  • Norbert Jankowski
  • Design of a Conscious Machine
  • P. Allen King
  • No Free Lunch versus Occam's Razor in Supervised Learning
  • Tor Lattimore and Marcus Hutter
  • An Approximation of the Universal Intelligence Measure
  • David L. Dowe
  • Shane Legg and Joel Veness
  • Minimum Message Length Analysis of the Behrens-Fisher Problem
  • Enes Makalic and Daniel F. Schmidt
  • MMLD Inference of Multilayer Perceptrons
  • Enes Makalic and Lloyd Allison
  • An Optimal Superfarthingale and Its Convergence over a Computable Topological Space
  • Kenshi Miyabe
  • Diverse Consequences of Algorithmic Probability
  • Eray Özkural
  • An Adaptive Compression Algorithm in a Deterministic World
  • Invited Papers.
  • Kristiaan Pelckmans
  • Toward an Algorithmic Metaphysics
  • Steve Petersen
  • Limiting Context by Using the Web to Minimize Conceptual Jump Size
  • Rafal Rzepka, Koichi Muramoto and Kenji Araki
  • Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models
  • Daniel F. Schmidt
  • Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction
  • Adrian Silvescu and Vasant Honavar
  • Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation
  • Ray Solomonoff and the New Probability
  • Alex Solomonoff
  • On the Application of Algorithmic Probability to Autoregressive Models
  • Ray J. Solomonoff and Elias G. Saleeby
  • Principles of Solomonoff Induction and AIXI
  • Peter Sunehag and Marcus Hutter
  • MDL/Bayesian Criteria Based on Universal Coding/Measure
  • Joe Suzuki
  • Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers
  • Hayato Takahashi
  • (Non-)Equivalence of Universal Priors
  • Grace Solomonoff
  • Ian Wood, Peter Sunehag and Marcus Hutter
  • A Syntactic Approach to Prediction
  • John Woodward and Jerry Swan
  • Short Paper.
  • Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory
  • Amiza Amir, Anang Hudaya M. Amin and Asad Khan
  • Universal Heuristics: How Do Humans Solve "Unsolvable" Problems?
  • Leonid A. Levin
Control code
SPR864821543
Dimensions
unknown
Extent
1 online resource (xvi, 445 pages)
File format
unknown
Form of item
online
Isbn
9783642449574
Level of compression
unknown
Other control number
10.1007/978-3-642-44958-1
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote

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