Machine Learning for Intelligent Decision Science

This book PDF is perfect for those who love Technology & Engineering genre, written by Jitendra Kumar Rout and published by Springer Nature which was released on 02 April 2020 with total hardcover pages 219. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Machine Learning for Intelligent Decision Science books below.

Machine Learning for Intelligent Decision Science
Author : Jitendra Kumar Rout
File Size : 53,5 Mb
Publisher : Springer Nature
Language : English
Release Date : 02 April 2020
ISBN : 9789811536892
Pages : 219 pages
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Machine Learning for Intelligent Decision Science by Jitendra Kumar Rout Book PDF Summary

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

Machine Learning for Intelligent Decision Science

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and

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Progress in Intelligent Decision Science

This book contains the topics of artificial intelligence and deep learning that do have much application in real-life problems. The concept of uncertainty has long been used in applied science, especially decision making and a logical decision must be made in the field of uncertainty or in the real-life environment

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The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep

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Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great

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Intelligent Decision Technologies

Intelligent Decision Technologies (IDT) seeks an interchange of research on intelligent systems and intelligent technologies which enhance or improve decision making in industry, government and academia. The focus is interdisciplinary in nature, and includes research on all aspects of intelligent decision technologies, from fundamental development to the applied system. This

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