Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Meta-learning via Search Combined with Parameter Optimization
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Meta-learning with Machine Generators and Complexity Controlled Exploration
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Projection Pursuit Constructive Neural Networks Based on Quality of Projected Clusters
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Almost Random Projection Machine
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Learning highly non-separable Boolean functions using constructive feedforward neural network
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
Support feature machine for DNA microarray data
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Almost random projection machine with margin maximization and kernel features
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Infosel++: information based feature selection C++ library
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Recursive similarity-based algorithm for deep learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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All existing learning methods have particular bias that makes them suitable for specific kind of problems. Universal Learning Machine (ULM) should find the simplest data model for arbitrary data distributions. Several ways to create ULMs are outlined, and an algorithm based on creation of new global and local features combined with meta-learning is introduced. This algorithm is able to find simple solutions that sophisticated algorithms ignore, learn complex Boolean functions, complicated probability distributions, as well as the problems requiring multiresolution decision borders.