Titlebook: Empirical Approach to Machine Learning; Plamen P. Angelov,Xiaowei Gu Book 2019 Springer Nature Switzerland AG 2019 Empirical Data Analytic

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Introduction,d, 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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Anomaly Detection—, Approach. and/or on the ., and in the second stage, the local anomalies are identified based on the . formed from the potential global anomalies. In addition, a fully autonomous approach for the problem of fault detection has been outlined, which can also be extended to a fully autonomous fault detection and isolation approach.

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1860-949X ly adapted to various applications.Covers techniques with hiThis book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduct

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On Model-Based Software Developmentin detecting collective anomalies as well as individual anomalies. The pseudo-code of the main procedure of the AAD algorithm and the MATLAB implementation can be found in Appendices?B.1 and C.1, respectively.

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https://doi.org/10.1007/978-3-030-57054-5quality data partitioning results in a highly efficient, objective manner. The ADP algorithm can also be used for classification even when there is very little supervision available. The pseudo-code of the main procedure of the ADP algorithm and the MATLAB implementations can be found in appendices B.2 and C.2, respectively.

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Empirical Fuzzy Sets and Systems effectively combine the data- and human-derived models and minimize the involvement of human expertise. They have significant advantages over the traditional ones because of the very strong interpretability, high objectiveness, being data driven and free from . assumptions.