Randomized Algorithms in Automatic Control and Data Mining [electronic resource] / by Oleg Granichin, Zeev (Vladimir) Volkovich, Dvora Toledano-Kitai.

By:

Granichin, Oleg [author.]

Contributor(s):

Series: Intelligent Systems Reference Library ; 67Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015Description: XXIV, 251 p. 99 illus., 19 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783642547867

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

006.3 23

LOC classification:

Q342

Online resources:

Click here to access online

Contents:

Randomized algorithms -- Randomization in estimation, identification and filtering problems under arbitrary external noises -- Data mining.

In: Springer eBooksSummary: In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.

Item type:

eBooks

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Randomized algorithms -- Randomization in estimation, identification and filtering problems under arbitrary external noises -- Data mining.

In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.