Universal Meta-Learning Architecture and Algorithms

There are hundreds of algorithms within data mining. Some of them are used to transform data, some to build classifiers, others for prediction, etc. Nobody knows well all these algorithms and nobody can know all the arcana of their behavior in all possible applications. How to find the best combination of transformation and final machine which solves given problem?

[1]  Kathleen Martin,et al.  The Learning Machines. , 1981 .

[2]  Norbert Jankowski,et al.  Heterogenous committees with competence analysis , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[3]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[4]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[5]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[6]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[7]  Jacek M. Zurada,et al.  Artificial Intelligence and Soft Computing, 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part I , 2010, International Conference on Artificial Intelligence and Soft Computing.

[8]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 1997, Texts in Computer Science.

[9]  N. Jankowski,et al.  Gained Knowledge Exchange and Analysis for Meta-Learning , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[10]  Norbert Jankowski,et al.  APPLICATIONS OF LEVIN ’ S UNIVERSAL OPTIMAL SEARCH ALGORITHM , 2000 .

[11]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[12]  Norbert Jankowski,et al.  Task Management in Advanced Computational Intelligence System , 2010, ICAISC.

[13]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[14]  Carlos Soares,et al.  Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results , 2003, Machine Learning.

[15]  Salvatore J. Stolfo,et al.  On the Accuracy of Meta-learning for Scalable Data Mining , 2004, Journal of Intelligent Information Systems.

[16]  Saso Dzeroski,et al.  Combining Classifiers with Meta Decision Trees , 2003, Machine Learning.

[17]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[18]  Erkki Oja,et al.  Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 , 2003, Lecture Notes in Computer Science.

[19]  Norbert Jankowski,et al.  Increasing Efficiency of Data Mining Systems by Machine Unification and Double Machine Cache , 2010, ICAISC.

[20]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[21]  Kate Smith-Miles,et al.  Towards insightful algorithm selection for optimisation using meta-learning concepts , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[22]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[23]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[24]  Hilan Bensusan,et al.  Meta-Learning by Landmarking Various Learning Algorithms , 2000, ICML.

[25]  Bogdan Gabrys,et al.  Learnt Topology Gating Artificial Neural Networks , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[26]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[27]  I. Guyon,et al.  Performance Prediction Challenge , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[28]  Włodzisław Duch,et al.  Committees of Undemocratic Competent Models , 2003 .

[29]  Paul M. B. Vitányi,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 1993, Graduate Texts in Computer Science.

[30]  Norbert Jankowski,et al.  Saving time and memory in computational intelligence system with machine unification and task spooling , 2011, Knowl. Based Syst..

[31]  Philip K. Chan,et al.  Meta-learning in distributed data mining systems: Issues and approaches , 2007 .

[32]  William I. Gasarch,et al.  Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series)) , 1997, SIGACT News.

[33]  Wodzisaw Duch,et al.  THE SEPARABILITY OF SPLIT VALUE CRITERION , 2000 .

[34]  David G. Stork,et al.  Pattern Classification , 1973 .

[35]  A. Kolmogorov Three approaches to the quantitative definition of information , 1968 .

[36]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[37]  Peter A. Flach,et al.  Improved Dataset Characterisation for Meta-learning , 2002, Discovery Science.

[38]  Hilan Bensusan,et al.  A Higher-order Approach to Meta-learning , 2000, ILP Work-in-progress reports.