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Magnetic-based detection technologies for undersea protection systems are very effective in monitoring critical areas where weak signal sources are difficult to identify (e.g. diver intrusion in proximity of the seafloor). The complexity of the involved geomagnetic phenomena and the nature of the target detection strategy require the use of adaptive methods for signal processing. The paper shows that...
Machine Learning based on the Regularized Least Squares (RLS) model requires one to solve a system of linear equations. Direct-solution methods exhibit predictable complexity and storage, but often prove impractical for large-scale problems; iterative methods attain approximate solutions at lower complexities, but heavily depend on learning parameters. The paper shows that applying the properties...
Information Retrieval is a well established interdisciplinary topic in which machine learning, computational linguistic, computer programming and data mining merge together. SLAIR stands for Sea Lab Advanced Information Retrieval and is an efficient software architecture that embeds these issues in a unique framework. SLAIR is expandable both from the data format and algorithm point of view. A pluggable...
Neural Networks are powerful tools for function approximation problems. A possible peculiar application of neural networks is that proposed here: estimating the univariate mean of a distribution from a finite sample. This problem characterizes a huge number of applicative and scientific problems. The Gaussian distribution case is analyzed, however the proposed analysis is of general validity and can...
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