Neural Modeling Fields
Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS). This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of the Cognition, mind's mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, Heterarchy, hetero-hierarchical system. At each level in NMF there are Schema (psychology), concept-models encapsulating the knowledge; they generate so-called Bottom–up and top–down design, top-down signals, interacting with input, bottom-up signals. These interactions are governed by Dynamical system, dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to ...
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Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ...
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