Abstract
Today we live in a data-rich environment. This is dramatically different from the last century when the fundamentals of machine learning , control theory and related subjects were established. Nowadays, vast and exponentially increasing data sets and streams which are often non-linear , non-stationary and increasingly more multi-modal /heterogeneous (combining various physical variables, signals with images/videos as well as text) are being generated, transmitted and recorded as a result of our everyday live. This is drastically different from the reality when the fundamental results of the probability theory , statistics and statistical learning where developed few centuries ago.
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Notes
- 1.
ALMMo-1 is using a single user-controlled parameter (\( \varOmega_{0} \)) which, however, has very little influence on the result and represents the standard for recursive least squares (RLS) algorithm initialization of the covariance matrix [28, 29]. Its value can easily be fixed to, for example, \( \varOmega_{0} = 10 \). This algorithm can optionally also use another two user-defined parameters (\( \eta_{o} \) and \( \varphi_{0} \)) which control the quality of the generated model.
- 2.
ALMMo-0 and the DRB classifiers use a single user-controlled parameter (\( r_{o} \)) which, however, has very little influence on the result and represents the initial radius of the area of influence of the new data cloud. Its value can easily be fixed to, \( r_{o} = \sqrt {2\left( {1 - \cos \left( {30^{o} } \right)} \right)} \). Moreover, it is only required if the ALMMo-0 and the DRB classifiers work online.
- 3.
SS_DRB classifier only requires two such parameters (\( \varOmega_{1} \) and \( \varOmega_{2} \)) but they carry clear meaning and suggested value ranges are provided.
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Angelov, P.P., Gu, X. (2019). Introduction. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_1
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