| [1] |
S.M. Bohte.
Spiking Neural Networks.
PhD thesis, Leiden University, 2003. [ bib ] |
| [2] |
Lars Willmes and Thomas Bäck.
Evolution strategies for engineering design optimization.
In K.J. Bathe, editor, Second M.I.T. Conference on Computational
Fluid and Solid Mechanics, June 17-20, Cambridge, MA. The MIT Press, 2003. [ bib ] |
| [3] |
Lars Willmes, Thomas Bäck, Yaochu Jin, and Bernhard Sendhoff.
Comparing neural networks and kriging for fitness approximation in
evolutionary optimization.
In R. Sarker, R. Reynolds, H. Abbass, K.C. Tan, B. McKay, and
T. Gedeon D. Essam, editors, The 2003 Congress on Evolutionary
Computation, CEC 2003, December 9-12, Canberra, Australia, pages 663-670.
IEEE Press, Piscataway, NJ, 2003. [ bib ] |
| [4] |
Lars Willmes and Thomas Bäck.
Multi-criteria airfoil design with evolution strategies.
In C.M. Fonseca, P.J. Fleming, E. Zitzler, K. Deb, and L. Thiele,
editors, Evolutionary Multi-Criterion Optimization, EMO 2003, Second
International Conference, April 8-11, Faro, Portugal, pages 782-795.
Springer, Berlin, 2003. [ bib ] |
| [5] |
Thomas Bäck, Claus Hillermeier, and Jörg Ziegenhirt.
Routing optimization in corporate networks by evolutionary
algorithms.
In A. Ghosh and S. Tsutsui, editors, Advances in Evolutionary
Computing - Theory and Applications, Natural Computing Series, pages
739-753. Springer, Berlin, 2003. [ bib ] |
| [6] |
Siegfried Nijssen and Thomas Bäck.
An analysis of the behaviour of simplified genetic algorithms on trap
functions.
IEEE Transactions on Evolutionary Computation, 7(1):11-22,
2003. [ bib ] |
| [7] |
Thomas Bäck.
Prozessoptimierung mit evolutionären Algorithmen: Prinzipien und
Anwendungsbeispiele.
In GMA-Kongress 2003 - Automation und Information in Wirtschaft
und Gesellschaft, Baden-Baden, Juni 2003, volume 1756 of
VDI-Berichte. VDI-Verlag, Düsseldorf, 2003. [ bib ] |
| [8] |
E.V. Samsonova, T. Bäck, A.P. Ijzerman, and J.N. Kok.
Combining and comparing cluster methods in a receptor database.
In Proceedings of the 5th International Conference on
Intelligent Data Analysis (IDA), volume 2810 of Lecture Notes in
Computer Science. Springer, Berlin, 2003. [ bib ] |
| [9] |
S. Nijssen and J.N. Kok.
Efficient frequent query discovery in FARMER.
In Proceedings of the PKDD 2003, volume 2431 of Lecture
Notes in Artificial Intelligence, pages 350-362. Springer-Verlag, 2003. [ bib ]
The upgrade of frequent item set mining to a setup with multiple relations -frequent query mining- poses many efficiency prob-lems. Taking Object Identity as starting point, we present several optimization techniques for frequent query mining algorithms. The resulting algorithm has a better performance than a previous ILP algorithm and competes with more specialized graph mining algorithms in performance.
|
| [10] |
S. Nijssen and J.N. Kok.
Proper refinement of datalog clauses using primary keys.
In Proceedings of the 15th Belgium/Netherlands Conference on
Artificial Intelligence (BNAIC'03), Nijmegen, The Netherlands, 2003. [ bib ]
Inductive Logic Programming (ILP) algorithms are frequently used for data mining in multi-relational databases. However, by most ILP algorithms primary key information is disregarded while this information is often available for such databases. This work shows how primary key information can be incorporated in a downward refinement operator. We show how primary keys can be used to define a sublanguage of full clausal logic, and provide evidence that one can define ideal refinement operators for this sublanguage.
|
| [11] |
S. Nijssen and J.N. Kok.
Efficient discovery of frequent unordered trees.
In Proceedings of the first International Workshop on Mining
Graphs, Trees and Sequences (MGTS'03), 2003. [ bib ]
Recently, an algorithm called Freqt was introduced which enumerates all frequent induced subtrees in an ordered data tree. We propose a new algorithm for mining unordered frequent induced subtrees. We show that the complexity of enumerating unordered trees is not higher than the complexity of enumerating ordered trees; a strategy for determining the frequency of unordered trees is introduced.
|
| [12] |
S. Nijssen and J.N. Kok.
Efficient discovery of frequent unordered trees: Proofs.
Technical Report 1, Leiden Institute of Advanced Computer Science,
Universiteit Leiden, 2003. [ bib ] |
| [13] |
W.A. Kosters and W. Pijls.
Apriori: A depth first implementation.
In B. Goethals and M.J. Zaki, editors, Proceedings of FIMI'03,
the first Workshop on Frequent Itemset Mining Implementations, Melbourne,
Florida, USA (CEUR Workshop Proceedings), 2003. [ bib ] |
| [14] |
M.C. van Wezel and W.A. Kosters.
Nonmetric multidimensional scaling: Neural networks versus
traditional techniques.
In T. Heskes, P. Lucas, L. Vuurpijl, and W. Wiegerinck, editors,
Proceedings of BNAIC 2003, the Fifteenth Belgium-Netherlands Artificial
Intelligence Conference, Nijmegen, The Netherlands, pages 331-338, 2003. [ bib ] |
| [15] |
J. Eggermont, J.N. Kok, and W.A. Kosters.
Genetic programming for data classification: Refining the search
space.
In T. Heskes, P. Lucas, L. Vuurpijl, and W. Wiegerinck, editors,
Proceedings of the 15th Belgium/Netherlands Conference on Artificial
Intelligence (BNAIC'03), pages 123-130, Nijmegen, The Netherlands, 23-24
October 2003. [ bib ] |
| [16] |
W.A. Kosters, W. Pijls, and V. Popova.
Complexity analysis of depth first and fp-growth implementations of
apriori.
In P. Perner and A. Rosenfeld, editors, Proceedings of MLDM 2003
(Machine Learning and Data Mining in Pattern Recognition), Leipzig, Germany,
number 2734 in LNAI, pages 284-292. Springer, 2003. [ bib ] |
| [17] |
M.C. van Wezel and W.A. Kosters.
Numerical integration by cubature formulae in Bayesian neural
networks.
In Supplementary Proceedings of ICONIP2003, Istanbul, Turkey,
pages 82-85, 2003. [ bib ] |
| [18] |
M. van der Nat and G. Rozenberg.
Gheorghe Paun and the Windmill Curiosity.
In C. Martín-Vide and V. Mitrana, editors, Grammars and Automata
for String Processing: from Mathematics and Computer Science to Biology and
Back, pages 1-5, London, 2003. Taylor and Francis. [ bib ] |
| [19] |
H.J. Hoogeboom and W.A. Kosters.
How to construct Tetris configurations.
Technical Report 8, Leiden Institute of Advanced Computer Science,
Universiteit Leiden, 2003. [ bib ] |
| [20] |
R. Breukelaar, H.J. Hoogeboom, and W.A. Kosters.
Tetris is hard, made easy.
Technical Report 9, Leiden Institute of Advanced Computer Science,
Universiteit Leiden, 2003. [ bib ] |
| [21] |
H.J. Hoogeboom and W.A. Kosters.
Tetris and decidability.
Technical Report 10, Leiden Institute of Advanced Computer Science,
Universiteit Leiden, 2003. [ bib ] |