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The MASA Approach to Machine Learning



In a nutshell, the term Machine Learning is commonly used to refer to any technique that serves to automatically extract information from a set of input data. The extracted information can take various forms: mathematical formulae, a set of rules or any logical or mathematical structure associated to the data.

MASA has developed, and has since continuously updated and improved, Machine Learning algorithms based on the statistical analysis of numerical data. MASA’s approach consists in representing information through mathematical models; that is, formulae. Since the problems that can be treated by our library algorithms are all those whose inputs can be translated into sets of real vectors, MASA’s framework is effectively capable of dealing with a very large class of interesting problems. The R&D work that MASA has been carrying out since 2001 is focused on the creation and application of learning algorithms, as well as the study of evaluation methods to assess the quality of the models.

The BK Machine Learning Toolkit



The BlueKaizen Machine Learning Toolkit is basically a framework of statistical learning components that can be used inside applications to solve problems in Supervised Classification, Clustering, Regression and Novelty Detection. Additionally, it contains components meant to help in the extraction of relevant information (or features) and model selection. The notion of statistical risk or quality of a model is essential to choose the best model from a set of candidate models. The extraction of features and the selection of the best model are steps common to all machine learning paradigms, as depicted below.


Some features of BlueKaizen Machine Learning:

The products



The first industrial applications considered by MASA for the BlueKaizen Machine Learning Toolkit are related to process control and quality monitoring, more specifically in the microelectronics and semiconductor industries.
BlueKaizen Machine Learning solutions have been tested on Fault Detection and Classification, Equipment Monitoring and Drift Detection and Advanced Process Control projects.