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Applications Of Synthetic High Dimensional Data Zdzislaw Polkowski

  • SKU: BELL-56731332
Applications Of Synthetic High Dimensional Data Zdzislaw Polkowski
$ 31.00 $ 45.00 (-31%)

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Applications Of Synthetic High Dimensional Data Zdzislaw Polkowski instant download after payment.

Publisher: Engineering Science Reference
File Extension: PDF
File size: 8.42 MB
Author: Zdzislaw Polkowski, Sambit Kumar Mishra, Marzena Sobczak-Michalowska, Samarjeet Borah
ISBN: 9798369318867, 836931886X
Language: English
Year: 2024

Product desciption

Applications Of Synthetic High Dimensional Data Zdzislaw Polkowski by Zdzislaw Polkowski, Sambit Kumar Mishra, Marzena Sobczak-michalowska, Samarjeet Borah 9798369318867, 836931886X instant download after payment.

The need for tailored data for machine learning models is often unsatisfied, as it is considered too much of a risk in the real-world context. Synthetic data, an algorithmically birthed counterpart to operational data, is the linchpin for overcoming constraints associated with sensitive or regulated information. In high-dimensional data, where the dimensions of features and variables often surpass the number of available observations, the emergence of synthetic data heralds a transformation. Applications of Synthetic High Dimensional Data delves into the algorithms and applications underpinning the creation of synthetic data, which surpass the capabilities of authentic datasets in many cases. Beyond mere mimicry, synthetic data takes center stage in prioritizing the mathematical domain, becoming the crucible for training robust machine learning models. It serves not only as a simulation but also as a theoretical entity, permitting the consideration of unforeseen variables and facilitating fundamental problem-solving. This book navigates the multifaceted advantages of synthetic data, illuminating its role in protecting the privacy and confidentiality of authentic data. It also underscores the controlled generation of synthetic data as a mechanism to safeguard private information while maintaining a controlled resemblance to real-world datasets. This controlled generation ensures the preservation of privacy and facilitates learning across datasets, which is crucial when dealing with incomplete, scarce, or biased data. Ideal for researchers, professors, practitioners, faculty members, students, and online readers, this book transcends theoretical discourse. It accentuates practical aspects, prioritizing the basic applicability of synthetic high-dimensional data. Each chapter unveils a facet of synthetic data's prowess, from its impact on society to its role in machine learning applications. It provides a roadmap for navigating the nuanced terrain of data privacy, conti

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