Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Feynman Lectures on Computation
Feynman Lectures on Computation
Mutual Information in Learning Feature Transformations
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Missing or Incomplete Data
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A resource allocation framework for collective intelligence system engineering
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Electrostatic field framework for supervised and semi-supervised learning from incomplete data
Natural Computing: an international journal
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Despite recent successes and advancements in artificial intelligence and machine learning, this domain remains under continuous challenge and guidance from phenomena and processes observed in natural world. Humans remain unsurpassed in their efficiency of dealing and learning from uncertain information coming in a variety of forms, whereas more and more robust learning and optimisation algorithms have their analytical engine built on the basis of some nature-inspired phenomena. Excellence of neural networks and kernel-based learning methods, an emergence of particle-, swarms-, and social behaviour-based optimisation methods are just few of many facts indicating a trend towards greater exploitation of nature inspired models and systems. This work intends to demonstrate how a simple concept of a physical field can be adopted to build a complete framework for supervised and unsupervised learning methodology. An inspiration for artificial learning has been found in the mechanics of physical fields found on both micro and macro scales. Exploiting the analogies between data and charged particles subjected to gravity, electrostatic and gas particle fields, a family of new algorithms has been developed and applied to classification, clustering and data condensation while properties of the field were further used in a unique visualisation of classification and classifier fusion models. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some comparative testing over well-known real and artificial datasets.