Advances in minimum description length : theory and applications /
A source book for state-of-the-art MDL, including an extensive tutorial and recent theoretical advances and practical applications in fields ranging from bioinformatics to psychology.The process of inductive inference--to infer general laws and principles from particular instances--is the basis of s...
| Other Authors: | Grünwald, Peter D., Myunvg, In Jae, Pitt, Mark A. |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cambridge, Mass. :
MIT Press,
©2005.
|
| Series: | Neural information processing series
|
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Similar Items
The minimum description length principle /
by: Grünwald, Peter D.
Published: (2007)
by: Grünwald, Peter D.
Published: (2007)
Advances in minimum description length : theory and applications /
Published: (2005)
Published: (2005)
Advances in minimum description length : theory and applications /
Published: (2005)
Published: (2005)
The minimum description length principle /
by: Grünwald, Peter D.
Published: (2007)
by: Grünwald, Peter D.
Published: (2007)
Statistical and inductive inference by minimum message length /
by: Wallace, C. S. (Christopher S.), -2004
Published: (2005)
by: Wallace, C. S. (Christopher S.), -2004
Published: (2005)
Statistical and inductive inference by minimum message length /
by: Wallace, C. S. (Christopher S.), -2004
Published: (2005)
by: Wallace, C. S. (Christopher S.), -2004
Published: (2005)
Advanced mean field methods : theory and practice /
Published: (2001)
Published: (2001)
Graphical models for machine learning and digital communication /
by: Frey, Brendan J.
Published: (1998)
by: Frey, Brendan J.
Published: (1998)
Learning and generalisation : with applications to neural networks /
by: Vidyasagar, M. (Mathukumalli), 1947-
Published: (2003)
by: Vidyasagar, M. (Mathukumalli), 1947-
Published: (2003)
Learning Bayesian networks /
by: Neapolitan, Richard E.
Published: (2004)
by: Neapolitan, Richard E.
Published: (2004)
Approximation methods for efficient learning of Bayesian networks /
by: Riggelsen, Carsten
Published: (2008)
by: Riggelsen, Carsten
Published: (2008)
Advances in Bayesian networks /
Published: (2004)
Published: (2004)
Dataset shift in machine learning /
Published: (2009)
Published: (2009)
Learning kernel classifiers : theory and algorithms /
by: Herbrich, Ralf
Published: (2002)
by: Herbrich, Ralf
Published: (2002)
Semi-supervised learning /
Published: (2006)
Published: (2006)
An introduction to computational learning theory /
by: Kearns, Michael J.
Published: (1994)
by: Kearns, Michael J.
Published: (1994)
Deep learning patterns and practices /
by: Ferlitsch, Andrew
Published: (2021)
by: Ferlitsch, Andrew
Published: (2021)
Principles and labs for deep learning /
by: Huang, Shih-Chia
Published: (2021)
by: Huang, Shih-Chia
Published: (2021)
Demystifying deep learning : an introduction to the mathematics of neural networks /
by: Santry, Douglas J.
Published: (2024)
by: Santry, Douglas J.
Published: (2024)
Advances in neural information processing systems 14 : proceedings of the 2001 conference /
Published: (2002)
Published: (2002)
Bayesian artificial intelligence /
by: Korb, Kevin B.
Published: (2004)
by: Korb, Kevin B.
Published: (2004)
Learning machine translation /
Published: (2009)
Published: (2009)
Perturbations, optimization, and statistics /
Published: (2017)
Published: (2017)
Introduction to statistical relational learning /
Published: (2007)
Published: (2007)
Large-scale kernel machines /
Published: (2007)
Published: (2007)
Advances in Computing and Data Sciences : 6th International Conference, ICACDS 2022, Kurnool, India, April 22-23, 2022, Revised Selected Papers, Part I /
Published: (2022)
Published: (2022)
Advances in Swarm Intelligence : 13th International Conference, ICSI 2022, Xi'an, China, July 15-19, 2022, Proceedings, Part I /
Published: (2022)
Published: (2022)
Introduction to Logic Programming /
by: Genesereth, Michael, et al.
Published: (2020)
by: Genesereth, Michael, et al.
Published: (2020)
Optimization for machine learning /
Published: (2012)
Published: (2012)
Learning with kernels : support vector machines, regularization, optimization, and beyond /
by: Schölkopf, Bernhard
Published: (2002)
by: Schölkopf, Bernhard
Published: (2002)
Embedding Reservoir Physics into Machine Learning /
by: Rocha Coutinho, Emilio Jose
Published: (2023)
by: Rocha Coutinho, Emilio Jose
Published: (2023)
Recurrent neural networks for prediction : learning algorithms, architectures and stability /
by: Mandic, Danilo P.
Published: (2001)
by: Mandic, Danilo P.
Published: (2001)
Neural networks and machine learning /
Published: (1998)
Published: (1998)
Recurrent neural networks for prediction : learning algorithms, architectures, and stability /
by: Mandic, Danilo P.
Published: (2001)
by: Mandic, Danilo P.
Published: (2001)
Gaussian processes for machine learning /
by: Rasmussen, Carl Edward
Published: (2006)
by: Rasmussen, Carl Edward
Published: (2006)
Neural smithing : supervised learning in feedforward artificial neural networks /
by: Reed, Russell D.
Published: (1999)
by: Reed, Russell D.
Published: (1999)
Advances in neural information processing systems 19 : proceedings of the 2006 conference /
Published: (2007)
Published: (2007)
Artificial neural networks : learning algorithms, performance evaluation, and applications /
by: Karayiannis, N. B. (Nicolaos B.), 1960-
Published: (1993)
by: Karayiannis, N. B. (Nicolaos B.), 1960-
Published: (1993)
Bayesian networks and decision graphs /
by: Jensen, Finn V.
Published: (2001)
by: Jensen, Finn V.
Published: (2001)
Biomedical and Computational Biology : Second International Symposium, BECB 2022, Virtual Event, August 13-15, 2022, Revised Selected Papers /
Published: (2023)
Published: (2023)