Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Supervised Learning: Mathematical Foundations and Real-World Applications

Supervised Learning: Mathematical Foundations and Real-World Applications

Chakrabarty, Dalia

Hardcover

Business GeneralGeneral Computers

ISBN10: 1032283300
ISBN13: 9781032283302
Publisher: CRC Press
Published: Mar 17 2025
Pages: 344
Weight: 1.45
Height: 0.81 Width: 6.14 Depth: 9.21
Language: English

This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the output and input variables, where said learning is undertaken within the remit of real-world information that can be messy in different ways. For example, the available data may be highly multivariate or be high-dimensional, reflecting the nature of the output variable that could be a vector, or matrix, or even higher in dimension, as is often the case in a real-world application. Additionally, the data is noisy, and often it is small to moderately large in size in multiple applications. Another difficulty that regularly arises is that the training dataset - comprising pairs of values of the input and output -is such, that the sought function cannot be captured by a parametric shape, but is instead underlined by an inhomogeneous correlation structure. These difficulties notwithstanding, we desire a streamlined methodology that allows the learning of the inter variable relationship - to ultimately permit fast and reliable predictions of the output, at newly recorded values of the input. In fact, occasions arise when one seeks values of the input at which a new output value is recorded, and such a demand is also addressed in the book.

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