A randomized approximation of the MDL for stochastic models with hidden variables
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Mutual information, Fisher information, and population coding
Neural Computation
Minimax regret under log loss for general classes of experts
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Text classification using ESC-based stochastic decision lists
Proceedings of the eighth international conference on Information and knowledge management
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining from open answers in questionnaire data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Logic, Language and Information
On predictive distributions and Bayesian networks
Statistics and Computing
Worst-Case Bounds for the Logarithmic Loss of Predictors
Machine Learning
Mining Open Answers in Questionnaire Data
IEEE Intelligent Systems
Text classification using ESC-based stochastic decision lists
Information Processing and Management: an International Journal
Classification of binary vectors by using ΔSC distance to minimize stochastic complexity
Pattern Recognition Letters
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Generalized Shannon Code Minimizes the Maximal Redundancy
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Extended Stochastic Complexity and Minimax Relative Loss Analysis
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
The Last-Step Minimax Algorithm
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
On-Line Estimation of Hidden Markov Model Parameters
DS '00 Proceedings of the Third International Conference on Discovery Science
Signal Processing - Special issue: Genomic signal processing
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting a binary sequence almost as well as the optimal biased coin
Information and Computation
Topic analysis using a finite mixture model
Information Processing and Management: an International Journal
The subspace information criterion for infinite dimensional hypothesis spaces
The Journal of Machine Learning Research
Optimality of universal Bayesian sequence prediction for general loss and alphabet
The Journal of Machine Learning Research
On some properties of the NML estimator for Bernoulli strings
Information Processing Letters
Uncertainty Modeling and Model Selection for Geometric Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on the applied use of MDL approximations
Neural Computation
Topic analysis using a finite mixture model
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Subspace Information Criterion for Model Selection
Neural Computation
Predictability, Complexity, and Learning
Neural Computation
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
MDL convergence speed for Bernoulli sequences
Statistics and Computing
Supervised evaluation of Voronoi partitions
Intelligent Data Analysis
Application of the Fisher-Rao Metric to Structure Detection
Journal of Mathematical Imaging and Vision
A lower bound on compression of unknown alphabets
Theoretical Computer Science
A Bayesian belief network for IT implementation decision support
Decision Support Systems
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
A linear-time algorithm for computing the multinomial stochastic complexity
Information Processing Letters
Application of the evidence procedure to the estimation of wireless channels
EURASIP Journal on Applied Signal Processing
NML computation algorithms for tree-structured multinomial Bayesian networks
EURASIP Journal on Bioinformatics and Systems Biology
Consistency of discrete Bayesian learning
Theoretical Computer Science
Decomposable Families of Itemsets
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Latent features in similarity judgments: A nonparametric bayesian approach
Neural Computation
A fast normalized maximum likelihood algorithm for multinomial data
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Integrating spatial and color information in images using a statistical framework
Expert Systems with Applications: An International Journal
IEEE Transactions on Signal Processing
Joint universal lossy coding and identification of stationary mixing sources with general alphabets
IEEE Transactions on Information Theory
Universal models for the exponential distribution
IEEE Transactions on Information Theory
Linear Time Model Selection for Mixture of Heterogeneous Components
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Fisher information determinant and stochastic complexity for Markov models
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
Minimum description length and clustering with exemplars
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Finiteness of redundancy, regret, Shtarkov sums, and Jeffreys integrals in exponential families
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Model selection by sequentially normalized least squares
Journal of Multivariate Analysis
On the consistency of discrete Bayesian learning
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
Fast NML computation for Naive Bayes models
DS'07 Proceedings of the 10th international conference on Discovery science
Learning locally minimax optimal Bayesian networks
International Journal of Approximate Reasoning
Gaussian clusters and noise: an approach based on the minimum description length principle
DS'10 Proceedings of the 13th international conference on Discovery science
Model-based multidimensional clustering of categorical data
Artificial Intelligence
On supervised selection of Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Minimum encoding approaches for predictive modeling
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The missing consistency theorem for bayesian learning: stochastic model selection
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Is there an elegant universal theory of prediction?
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Perceptual learning inspired model selection method of neural networks
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Using context and phonetic features in models of etymological sound change
EACL 2012 Proceedings of the EACL 2012 Joint Workshop of LINGVIS & UNCLH
Botnet detection based on non-negative matrix factorization and the MDL principle
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Minimum message length inference and mixture modelling of inverse gaussian distributions
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Project dynamics and emergent complexity
Computational & Mathematical Organization Theory
| Hi-index | 754.96 |
By taking into account the Fisher information and removing an inherent redundancy in earlier two-part codes, a sharper code length as the stochastic complexity and the associated universal process are derived for a class of parametric processes. The main condition required is that the maximum-likelihood estimates satisfy the central limit theorem. The same code length is also obtained from the so-called maximum-likelihood code