- Remco R. Bouckaert.
DensiTree: Making Sense of Sets of Phylogenetic Trees.
Bioinformatics 2010; doi: 10.1093/bioinformatics/btq110
(accompanying software and manual)
- Remco R. Bouckaert: A Hierarchical Face Recognition Algorithm. ACML 2009: 38-50
- Eibe Frank, Remco R. Bouckaert: Conditional Density Estimation with Class Probability Estimators. ACML 2009: 65-81
- R. Bouckaert, G. Holmes, B. Pfahringer and D. Fletcher.
Gaussian Processes on Graphics Cards for NIR.
- B. Pfahringer, D. Fletcher, R.Bouckaert, G.Holmes
Random Model Trees: a competitive off-the-shelf technology for NIR
- Remco R. Bouckaert: Practical Bias Variance Decomposition
In Proc 21st Australasian Joint Conference on Artificial Intelligence
Auckland, New Zealand, 2008
- Remco R. Bouckaert, Milan Studeny: Racing algorithms for conditional
independence inference. Int. J. Approx. Reasoning 45(2): 386-401 (2007)
- Remco R. Bouckaert, Michael Goebel, Patricia J. Riddle: Generalized
Unified Decomposition of Ensemble Loss. Australian Conference on
Artificial Intelligence 2006: 1133-1139
pdf extended version
- Remco R. Bouckaert: Efficient AUC Learning Curve Calculation. Australian
Conference on Artificial Intelligence 2006: 181-191
- Remco R. Bouckaert: Voting Massive Collections of Bayesian Network
Classifiers for Data Streams. Australian Conference on Artificial
Intelligence 2006: 243-252
- Remco R. Bouckaert.
Low Replicability of Machine Learning Experiments is not a Small Data Set Phenomenon.
ICML Meta Learning workshop, 2005.
- Remco R. Bouckaert and Milan Studeny.
Racing for conditional independence inference.
ECSQARU 221-232, 2005. Demonstration applets.
- Remco R. Bouckaert, Bayesian networks in Weka. Technical Report 14/2004. Computer Science Department. University of Waikato. 2004.
- Remco R. Bouckaert, Naive Bayes Classifiers that Perform Well with Continuous Variables. In Proceedings of the 17th Australian Conference on AI (AI 04), Lecture Notes in AI, Springer. 2004.
Extended report version
- Remco R. Bouckaert, Estimating Replicability of Classifier Learning Experiments. ICML, 2004.
Remco R. Bouckaert and Eibe Frank, Evaluating the Replicability of Significance
Tests for Comparing Learning Algorithms. PAKDD, 2004. ps
- Remco R. Bouckaert, Choosing between two learning algorithms based on calibrated tests,
International Conference on Machine Learning, 51--58, 2003.
Extended report version
Data used for the experiments
- Remco R. Bouckaert,
A Probabilistic Line Breaking Algorithm,
The 16th Australian Joint Conference on Artificial Intelligence - AI.03, 2003.
pdf Extended report version
- Remco R. Bouckaert, Choosing learning algorithms using sign tests with high replicability,
The 16th Australian Joint Conference on Artificial Intelligence - AI.03, 2003.postscript
- Accuracy bounds for ensembles under 0 - 1 loss,
Remco R. Bouckaert,
Working Paper 02/04,
Computer science department,
University of Waikato postscript
- R. R. Bouckaert. Low level information extraction: a Bayesian network based approach. TextML 2002. pdf
- Tomas Kocka, Remco R. Bouckaert, Milan Studeny. On characterizing Inclusion of Bayesian Networks.
Uncertainty in artificial intelligence. 2001. pdf
- M. Studeny, R.R. Bouckaert, T. Kocka: Extreme supermodular set functions over five variables. Research report n. 1977, Institute of Information Theory and Automation, Prague, January 2000 (32 pages).
- M. Studeny and R. R. Bouckaert: On chain graph models for description of conditional independence structures. The Annals of Statistics 26 (1998), n. 4, pp. 1434-1495.
Bayesian networks in Weka.
There is some user documentation in pdf format
and on line. Now with Bayes network editor GUI!
Erdos number: 3
3: Enrique Castillo, Remco R. Bouckaert, Jose M. Sarabia, Cristina Solares: Error Estimation in Approximate Bayesian Belief Network Inference. UAI 1995: 55-62
2: E. Castillo and J. Galambos. Lifetime regression models based on a functional equation of physical nature. Journal of Applied Probability. 24:160-169, 1987.
1: Erdos, Paul; Galambos, Janos , Asymptotic distribution of normalized arithmetical functions. Proc. Am. Math. Soc. 46, 1-8 (1974).
Matrix inverse with CUDA and Cublas. Invert a 10.000x10.000 symmetric positive definite matrix in under 15 seconds on a NVidia GTX 280 card (14.38 seconds will do).
source code . Also contain matrix inversion through Gaussian elimination for nxn matrices.
Other open source projects
BEAST 2: MCMC and evolution libraries for Bayesian phylogenetic analysis.
Snap: a Beast 2 add on for inferring species trees directly from SNP and AFLP data.
Check out ShoXS, a
Shorter XSL Syntax.