Exploring logical dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Neural networks and intellect: using model-based concepts
Neural networks and intellect: using model-based concepts
Aircraft Detection: A Case Study in Using Human Similarity Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Dynamic Logic
Visual and Spatial Analysis
Symbolic methodology for numeric data mining
Intelligent Data Analysis - Philosophies and Methodologies for Knowledge Discovery
Relational methodology for data mining and knowledge discovery
Intelligent Data Analysis - Philosophies and Methodologies for Knowledge Discovery
Multisensor Data Fusion
Dynamic Epistemic Logic
Evolution of Languages, Consciousness and Cultures
IEEE Computational Intelligence Magazine
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Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The Neural Modeling Fields (NMF) theory and Dynamic Logic of Phenomena (DLP) address these challenges in a non-traditional way. The main idea behind their success is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more certain then the evaluation criterion is also adjusted dynamically to match the adjusted model. This process mimics processes of the mind and natural evolution at the neural level. This paper describes the generalization of DLP for data fusion and mining of heterogeneous spatial objects in cyber-physical space.