Electronics in Advanced Research Industries. Alessandro Massaro

Electronics in Advanced Research Industries - Alessandro Massaro


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are “extracted” by more accurate inspections and by the application of specific intelligent image processing algorithms enhancing anomalous features. Intelligent algorithms are usually named machine learning (ML). In Table 1.6 the main ML unsupervised and supervised algorithms are classified [42]. The supervised learning algorithm processes a known input dataset and data outputs to learn the regression/classification model. In supervised learning approaches, the training is performed by “labelled” data, selecting specific variables to focus the analysis: some data are already tagged with the requested answer, and the labeled data are adopted for the self‐learning of the algorithms predicting outcomes of the labeled variables. Unsupervised learning is the training modality of the algorithm which processes a dataset that is not classified/labeled. In the unsupervised learning approaches the model does not need to be supervised: the models discover information and common features of the variables (attributes) and find all kinds of unknown patterns in the data. The learning phase is structured in the following sequential steps:

       Training dataset construction.

       Features vector extraction.

       Algorithm application setting data processing parameters.

       Training model construction.

Machine learning algorithm class Unsupervised Supervised
Continuous Clustering:K‐meansMean shift clusteringDensity‐based spatial clustering of applications with noiseExpectation maximization Clustering using gaussian mixture modelsAgglomerative hierarchical clusteringDimensionality reduction:Principal component analysisSingular value decomposition Linear regressionPolynomial regressionArtificial neural networkRandom forestsDecision trees
Categorical Association analysis:AprioriFP‐growthHidden Markov model Classification:k‐nearest neighborsDecision treesLogistic regressionNaïve BayesArtificial neural networkSupport vector machine

      Both classes of supervised and unsupervised algorithms are typically applied for data processing applications of image processing for feature classification.

      (1.1)equation

      The


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