• Title Introduction to Soft Computing
• Author(s) Eva Volna
• Publisher: VEER SURENDRA SAI UNIVERSITY OF TECHNOLOGY
• Hardcover: N/A
• eBook: PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-8740303919
• Share This:  

Book Description

This book gives an introduction to Soft Computing, which aims to exploit tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve close resemblance with human like decision making. Soft Computing is a new multidisciplinary field, to construct new generation of Artificial Intelligence, known as Computational Intelligence.

• EVA VOLNA is an associate professor at the Department of Computer Science at University of Ostrava, Czech Republic. Her interests include artificial intelligence, artificial neural networks, evolutionary algorithms, and cognitive science. She is an author of more than 50 papers in technical journals and proceedings of conferences.

• Amazon

• Theory of Computation and Computing
• Artificial Intelligence, Machine Learning, and Logic Programming
• Mathematical Logic - Set Theory, Model Theory, Computability, etc
• Introduction to Computer Science

• Introduction to Soft Computing (Eva Volna)
• The Mirror Site (1) - PDF

• Soft Computing: Techniques in Engineering Sciences

  Soft Computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. This book elaborates on the most recent applications of Soft Computing in various fields of engineering.

• Applicative Computing: Its Quarks, Atoms and Molecules

  This work covers the advanced topics in main ideas of computing in general. Material is especially useful for the instructor, postgraduate and graduate students of IT-specialties and is suitable for the system of training of specialists.

• Natural Computing and Beyond (Yasuhiro Suzuki, et al)

  This book compiles refereed contributions to various aspects of Natural Computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics.

• Introduction to Classical and Quantum Computing (Tom Wong)

  This book is for students who want to learn quantum computing beyond a conceptual level, but who lack advanced training in mathematics. The only prerequisite is trigonometry, and mathematics beyond that will be covered.

• Models of Computation: Exploring the Power of Computing

  It covers the traditional topics of formal languages, automata and complexity classes, as well as an introduction to the more modern topics of space-time tradeoffs, memory hierarchies, parallel computation, the VLSI model, and circuit complexity.

• Foundations of Machine Learning (Mehryar Mohri, et al)

  This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

• Machine Learning Yearning (Andrew Ng)

  You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.