Statistical Analysis of High Dimensional Data With Case Studies Using Machine Learning Algorithms |
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| Author:
| Tiwari, Anurag
Chaturvedi, Amrita |
| Series title: | Intelligent Data-Centric Systems Ser. |
| ISBN: | 978-0-323-85418-4 |
| Publication Date: | Jun 2024 |
| Publisher: | Elsevier Science & Technology Books
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| Imprint: | Academic Press |
| Book Format: | Ebook |
| List Price: | USD $130.00USD $156.00 |
Book Description:
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Statistical Analysis of High Dimensional Data with Case Studies Using Machine Learning Algorithms explains some newly developed Machine Learning algorithms and associated scope of optimization with input data samples. These algorithms provide better time and space complexity as compared to some of earlier developed algorithms like K-NN. Based on these case studies and approaches, a reader can resolve several optimization problems in the domain of AI.
Dealing with high...
More Description
Statistical Analysis of High Dimensional Data with Case Studies Using Machine Learning Algorithms explains some newly developed Machine Learning algorithms and associated scope of optimization with input data samples. These algorithms provide better time and space complexity as compared to some of earlier developed algorithms like K-NN. Based on these case studies and approaches, a reader can resolve several optimization problems in the domain of AI.
Dealing with high dimensional data such as high contrast medical images, neural data, and microarray data is very common in recent applications. To efficiently deal with such data observations, this book will assist the reader in two ways: (1) Provide a precise understanding of problem and scope of ML algorithms, (2) Describe various new concepts such as convex modeling, different types of regularization methods that can be used in those situations where traditional ML algorithms fail to provide efficient results.
This book covers the basic as well as some advanced concepts of machine learning algorithms specifically designed for high dimensional data. To explore the significance of these algorithms in a random machine learning problem, it presents these approaches in a very generalized manner.
- Explains various machine learning algorithms mathematically from a statistical perspective.
- Provides a powerful world created by statistics and machine learning with emphasis on mathematical derivations to define the underlying logic behind an algorithm.
- Describes algorithms that process and analyse high dimensional data.
- Presents case studies carrying application of machine learning approaches onto different domains.