Meta-learning

Books/chapters:

• Jankowski N, Duch W, Grąbczewski K, Meta-learning in Computational Intelligence.
  Studies in Computational Intelligence, Vol. 358, 1st Edition, pp. X + 362. 127 illus, 76 in color, Springer 2011.
• Jankowski N, Grąbczewski K, Universal Meta-Learning Architecture and Algorithms, In: Meta-Learning in Computational Intelligence, Editors: Jankowski N, Duch W, Grąbczewski K, Studies in Computational Intelligence, Springer Berlin / Heidelberg, 2011, Vol. 358, pp. 1-76.

• Duch W, Maszczyk T, Grochowski M, Optimal Support Features for Meta-Learning.
  Book chapter, in: Meta-learning in Computational Intelligence. Studies in Computational Intelligence. Eds: N. Jankowski, K. Grabczewski, W. Duch, Springer 2011, pp. 317-358.
• Jankowski N. Meta-uczenie w inteligencji obliczeniowej (Meta-learning in computational intelligence). Warszawa, Polska: Akademicka Oficyna Wydawnicza EXIT, 2011, 396 pages (hopfully soon it will be in English).

Papers:

• Duch W, Meta-learning.
  Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springer 2013 (in print). Short intro to metalearning.
• Duch W, Maszczyk T, Jankowski N, Make it cheap: learning with O(nd) complexity.
  2012 IEEE World Congress on Computational Intelligence, Brisbane, Queensland, Australia, 10-15.06.2012, IJCNN (ISBN: 978-1-4673-1489-3), pp. 132-135.
• Grąbczewski K, Validated Decision Trees versus Collective Decisions. Lecture Notes in Computer Science, Springer 2011, Vol. 6923, pp. 342-351.

• Grąbczewski K, Jankowski N, Saving time and memory in computational intelligence system with machine unification and task spooling, Knowledge-Based Systems, 24(5), 570-588, 2011.

• Grąbczewski K, Unified View of Decision Tree Learning Machines for the Purpose of Meta-learning, In: Computer Recognition Systems 4, Editors: Burduk R, Kurzyński M, Woźniak M, Żołnierek A, Advances in Intelligent and Soft Computing, Springer Berlin / Heidelberg, 2011, Vol. 95, pp. 147-155.

• Jankowski N, Grąbczewski K, Increasing efficiency of meta-learning machines with complexity control. Lecture Notes in Computer Science, Springer 2010, pp. 380-387.

• Grąbczewski Krzysztof and Jankowski Norbert, Meta-learning with machine generators and complexity controlled exploration . In Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, pp. 545–555. Springer, 2008

• Jankowski N, Grąbczewski K, Building meta-learning algorithms basing on search controlled by machine's complexity and machines generators. IEEE World Congress on Computational Intelligence. 2008, pp. 3601-3608, doi: 10.1109/IJCNN.2008.4634313.

• Duch W, Towards comprehensive foundations of computational intelligence.
  In: W. Duch and J. Mandziuk, Challenges for Computational Intelligence. Springer Studies in Computational Intelligence, Vol. 63, 261-316, 2007.
• Duch W, Learning data structures with inherent complex logic: neurocognitive perspective.
  The 6th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (CIMMACS '07), Tenerife, Canary Islands, Spain, Dec. 14-16, 2007, pp. 294-303
• Grąbczewski K, Jankowski N, Versatile and Efficient Meta-Learning Architecture: Knowledge Representation and~Management in~Computational Intelligence, IEEE Symposium Series on Computational Intelligence (SSCI 2007), Honolulu, pp. 51-58.

• Grąbczewski K, and Jankowski N, Meta-learning as scheme-based search with complexity control. International Joint Conference on Neural Network. Workshop on Meta-Learning. 2007, pp. 3-8.

• Grąbczewski K, and Jankowski N, Control of complex machines for meta-learning in computational intelligence. Computational Intelligence, Man-Machine Systems and Cybernetics, 2007, pp. 287-293.

• Jankowski N, Grąbczewski K, Learning machines information distribution system with example applications, Computer Recognition Systems 2, Adavances in Soft Computing, Springer, 2007, pp. 205-215.

• Jankowski N, Grąbczewski K, Gained knowledge exchange and analysis for meta-learning, Proceedings of International Conference on Machine Learning and Cybernetics, IEEE Press, 2007, pp. 795-802.

• Duch W, Grudziński K, Meta-learning via search combined with parameter optimization.
  Inteligent Information Systems, Advances in Soft Computing, Physica Verlag (Springer) 2002, pp. 13-22
• Duch W, Grudziński K, Meta-learning: searching in the model space. Proceedings of the International Conference on Neural Information Processing, Shanghai, 2001, Vol. I, pp. 235-240

Papers related to heterogenous systems are also relevant here:

1. Duch W (2005) Uncertainty of data, fuzzy membership functions, and multi-layer perceptrons.
   IEEE Transactions on Neural Networks 16(1): 10-23.
   Relations between fuzzy logic and neural transfer functions are analyzed and a few new transfer functions derived.
2. Jankowski N, Grąbczewski K, Heterogenous Committees with Competence Analysis, Fifth International conference on Hybrid Intelligent Systems, Rio de Janeiro, Brasil, 2005, pp. 417-422.

3. Duch W, Jankowski N, Transfer functions: hidden possibilities for better neural networks. 9th European Symposium on Artificial Neural Networks (ESANN), Brugge 2001. De-facto publications, pp. 81-94
4. Duch W, Adamczak R, Diercksen GHF, Constructive density estimation network based on several different separable transfer functions. 9th European Symposium on Artificial Neural Networks (ESANN), Brugge 2001. De-facto publications, pp. 107-112
5. Jankowski N, Duch W, Optimal transfer function neural networks. 9th European Symposium on Artificial Neural Networks (ESANN), Brugge 2001. De-facto publications, pp. 101-106
6. Duch W, Adamczak R, Diercksen GHF, Neural Networks from Similarity Based Perspective.
   New Frontiers in Computational Intelligence and its Applications. Ed. M. Mohammadian, IOS Press, Amsterdam 2000, pp. 93-108
7. Duch W, Jankowski N, Taxonomy of neural transfer functions,
   IEEE, International Joint Conference on Neural Networks 2000 (IJCNN), Vol. III, pp. 477-484
8. Duch W, Jankowski N (1999) Survey of neural transfer functions,
   Neural Computing Surveys 2: 163-213, paper in PDF.
9. Duch W, Adamczak R, Diercksen GHF (1999) Neural Networks in non-Euclidean spaces.
   Neural Processing Letters  10: 201-210
10. Duch W, Adamczak R, Diercksen GHF (1999) Distance-based multilayer perceptrons.
    Computational Intelligence for Modelling Control and Automation. Neural Networks and Advanced Control Strategies. Ed. M. Mohammadian, IOS Press, Amsterdam, pp. 75-80
11. Duch W and Jankowski N (1996) Bi-radial transfer functions. | PDF file.
    Proceedings of the Second Conference on Neural Networks and their applications, Orle Gniazdo, 30.IV-4.V.1996, pp. 131-137

Other papers related to heterogenous systems:

1. Kordos M, Duch W (2004), A Survey of Factors Influencing MLP Error Surface.
   Control and Cybernetics 33(4): 611-631.
   Neural error functions are influence by the choice of transfer functions, but the primary goal here is to visualize the training process.
2. Duch W, Blachnik M, Fuzzy rule-based systems derived from similarity to prototypes.
   Lecture Notes in Computer Science, Vol. 3316 (2004) 912-917.
   Introduces transformations between distance functions and fuzzy membership functions that are used as transfer functions in the basis expansion networks, such as RBF or separable functions networks.
3. Duch W, Grabczewski K, Heterogeneous adaptive systems. IEEE World Congress on Computational Intelligence, Honolulu, May 2002, pp. 524-529.
4. Duch W, Similarity based methods: a general framework for classification, approximation and association,
   Control and Cybernetics 29 (4) (2000) 937-968
5. Duch W, Adamczak R, Diercksen G.H.F, Classification, Association and Pattern Completion using Neural Similarity Based Methods.
   Applied Mathematics and Computer Science 10:4 (2000) 101-120
6. N. Jankowski. Ontogenic neural networks and their applications to classification of medical data . PhD thesis, Department of Computer Methods, Nicolaus Copernicus University, Torun, Poland, 1999.
7. N. Jankowski. Approximation and classification in medicine with IncNet neural networks. In Machine Learning and Applications. Workshop on Machine Learning in Medical Applications, pp. 53-58, Chania, Greece, July 1999.
8. N. Jankowski. Flexible transfer functions with ontogenic neural. Technical report, Computational Intelligence Lab, DCM NCU, Torun, Poland, 1999.
9. N. Jankowski. Approximation with RBF-type neural networks using flexible local and semi-local transfer functions. In 4th Conference on Neural Networks and Their Applications, pp. 77-82, Zakopane, Poland, May 1999.
10. Duch W, Grudzinski K and Diercksen G.H.F (1998) Minimal distance neural methods.
    World Congress of Computational Intelligence, May 1998, Anchorage, Alaska, IEEE IJCNN'98 Proceedings, pp. 1299-1304
11. Duch W and Jankowski N (1997) New neural transfer functions.
    Applied Mathematics and Computer Science 7 (1997) 639-658 (invited by the Editor)
12. Duch W and Diercksen GHF (1995) Feature Space Mapping as a universal adaptive system. | PDF file
    Computer Physics Communications 87: 341-371
13. Duch W (1994) Floating Gaussian Mapping: a new model of adaptive systems.
    Neural Network World 4:645-654
14. Duch W (1993) On the optimal processing functions for neural network elements, UMK-KMK-TR 6/93 report.
15. Links to many talks and to other papers on various subjects.

• Other projects of the Department of Informatics, NCU.
• Maintained by Wlodzislaw Duch.
• Working log (local accsess only)