Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
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[1] W. Quine. The two dogmas of empiricism , 1951 .
[2] D M Boulton,et al. The information content of a multistate distribution. , 1969, Journal of theoretical biology.
[3] Nick Bostrom,et al. Whole Brain Emulation , 2008 .
[4] Ray J. Solomonoff,et al. The Discovery of Algorithmic Probability , 1997, J. Comput. Syst. Sci..
[5] P. Glaskowsky. NVIDIA ’ s Fermi : The First Complete GPU Computing Architecture , 2009 .
[6] Jorma Rissanen,et al. Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.
[7] G. Chaitin. Randomness and Mathematical Proof , 1975 .
[8] Hui Ding,et al. Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..
[9] Jonathan Miller,et al. MicroRNA Target Detection and Analysis for Genes Related to Breast Cancer Using MDLcompress , 2007, EURASIP J. Bioinform. Syst. Biol..
[10] Michael Small,et al. Minimum description length neural networks for time series prediction. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[11] Gregory J. Chaitin,et al. On the Length of Programs for Computing Finite Binary Sequences , 1966, JACM.
[12] Edwin P. D. Pednault,et al. Some Experiments in Applying Inductive Inference Principles to Surface Reconstruction , 1989, IJCAI.
[13] Douglas S. Bridges,et al. Constructive Mathematics and Quantum Physics , 2000 .
[14] C. S. Wallace,et al. An Information Measure for Single Link Classification , 1975, Comput. J..
[15] Dimitrios Gunopulos,et al. Streaming Time Series Summarization Using User-Defined Amnesic Functions , 2008, IEEE Transactions on Knowledge and Data Engineering.
[16] Eamonn J. Keogh,et al. Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs , 2010, 2010 IEEE International Conference on Data Mining.
[17] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 1997, Texts in Computer Science.
[18] D. Dowe,et al. A conceptual model for integrating physical geography research and coastal wetland management, with an Australian example , 2010 .
[19] Fabian Mörchen,et al. Optimizing time series discretization for knowledge discovery , 2005, KDD '05.
[20] S. Amari,et al. Network Information Criterion | Determining the Number of Hidden Units for an Articial Neural Network Model Network Information Criterion | Determining the Number of Hidden Units for an Articial Neural Network Model , 2007 .
[21] Eray Özkural. A compromise between reductionism and non-reductionism , 2007 .
[22] Lawrence B. Holder,et al. Attribute-Value Selection Based on Minimum Description Length , 2004, IC-AI.
[23] Chris S. Wallace,et al. A Program for Numerical Classification , 1970, Comput. J..
[24] Jürgen Schmidhuber,et al. Ultimate Cognition à la Gödel , 2009, Cognitive Computation.
[25] R. J. Solomon Off,et al. The time scale of artificial intelligence: Reflections on social effects , 1985 .
[26] Philip Chan,et al. Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.
[27] Jonathan J. Oliver,et al. MDL and MML: Similarities and differences , 1994 .
[28] David L. Dowe,et al. MML, hybrid Bayesian network graphical models, statistical consistency, invarianc , 2010 .
[29] Petri Myllymäki,et al. MDL Histogram Density Estimation , 2007, AISTATS.
[30] Gregory J. Chaitin. Two Philosophical Applications of Algorithmic Information Theory , 2003, DMTCS.
[31] Eamonn J. Keogh,et al. A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases , 2000, PAKDD.
[32] Jessica Lin,et al. Finding Motifs in Time Series , 2002, KDD 2002.
[33] Mark A. Pitt,et al. Advances in Minimum Description Length: Theory and Applications , 2005 .
[34] C. S. WALLACE,et al. Air Showers of Size Greater than 105 Particles: (I) Core Location and Shower Size Determination , 1958, Nature.
[35] Jean-Louis Dessalles,et al. Algorithmic Simplicity and Relevance , 2012, Algorithmic Probability and Friends.
[36] Gregory J. Chaitin,et al. On the Simplicity and Speed of Programs for Computing Infinite Sets of Natural Numbers , 1969, J. ACM.
[37] George Barmpalias,et al. Universality probability of a prefix-free machine , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[38] Eamonn J. Keogh,et al. iSAX 2.0: Indexing and Mining One Billion Time Series , 2010, 2010 IEEE International Conference on Data Mining.
[39] Andrew R. Barron,et al. Minimum complexity density estimation , 1991, IEEE Trans. Inf. Theory.
[40] David L. Dowe,et al. Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence” , 2008 .
[41] Anang Hudaya Muhamad Amin,et al. Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory , 2011, Algorithmic Probability and Friends.
[42] Héctor-Gabriel Acosta-Mesa,et al. Discretization of Time Series Dataset with a Genetic Search , 2009, MICAI.
[43] Li Wei,et al. Experiencing SAX: a novel symbolic representation of time series , 2007, Data Mining and Knowledge Discovery.
[44] Steven de Rooij,et al. Approximating Rate-Distortion Graphs of Individual Data: Experiments in Lossy Compression and Denoising , 2012, IEEE Transactions on Computers.
[45] C. S. Wallace,et al. An Information Measure for Hierarchic Classification , 1973, Comput. J..
[46] D. L. Donoho,et al. Ideal spacial adaptation via wavelet shrinkage , 1994 .
[47] M. H. Brennan. Data Processing in the Early Cosmic Ray Experiments in Sydney , 2008, Comput. J..
[48] Jorma Rissanen,et al. Density estimation by stochastic complexity , 1992, IEEE Trans. Inf. Theory.
[49] R. Solomonoff. Machine Learning — Past and Future , 2009 .
[50] Pasi Fränti,et al. Knee Point Detection in BIC for Detecting the Number of Clusters , 2008, ACIVS.
[51] Ira Assent,et al. The TS-tree: efficient time series search and retrieval , 2008, EDBT '08.
[52] R. Solomonoff. A PRELIMINARY REPORT ON A GENERAL THEORY OF INDUCTIVE INFERENCE , 2001 .
[53] Nikolai K. Vereshchagin,et al. Rate Distortion and Denoising of Individual Data Using Kolmogorov Complexity , 2010, IEEE Transactions on Information Theory.
[54] David Balduzzi,et al. Falsification and Future Performance , 2011, Algorithmic Probability and Friends.
[55] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[56] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[57] G. Chaitin. Gödel's theorem and information , 1982 .
[58] H. Akaike. Factor analysis and AIC , 1987 .
[59] David L. Dowe. Discussion on hedging predictions in machine learning by A Gammerman and V Vovk , 2007 .
[60] David L. Dowe,et al. Foreword re C. S. Wallace , 2008, Comput. J..
[61] C. S. Wallace,et al. Occupancy of a Rectangular Array , 1973, Comput. J..
[62] Douglas I. Campbell,et al. The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"? , 2011, Algorithmic Probability and Friends.
[63] David L. Dowe,et al. Minimum message length and generalized Bayesian nets with asymmetric languages , 2005 .
[64] C. S. Wallace,et al. An Information Measure for Classification , 1968, Comput. J..
[65] Eamonn J. Keogh,et al. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.