Principles and Theory for Data Mining and Machine Learning

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Digital twin paradigm: A systematic literature review (2021, background): “The machine learning refers to the ability to give computers the possibility to learn without being explicitly programmed (Clarke et al., 2009).”
Chime-home: A dataset for sound source recognition in a domestic environment (2015, methodology): “We require that annotators assign at least one label to a chunk, thus annotators may either assign one or more labels from the set { c,m,f,v,p,b,o } , or may alternatively ‘ﬂag’ the chunk using a single label from the set { S,U } .”
Handbook of Big Data (2016, background): “For example, a common explanation for the COD is that as p→∞, the volume of the unit hypersphere relative to that of the unit hypercube goes to zero [4].”
Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction (2012, methodology): “For further discussion on this and other similar approaches in machine learning, see [Clarke et al., 2009].”
Gaining insight with recursive partitioning of generalized linear models (2013, background): “This is why a number of flexible methods that only need very few assumptions have recently been developed (sometimes collected under the umbrella terms ‘data mining’ and ‘machine learning’ [4]).”
Applications of Artificial Intelligence in Machine Learning: Review and Prospect (2015, unknown): “[20] [37] [49]”