This book presents neural networks from a theoretical viewpoint with practical applications. Unlike other books, which focus on a single architecture or view neural networks primarily through a machine learning prism, this book provides a comprehensive view of neural networks from its roots in neuroscience to recent work on both connectivity theory and statistical classification theory. Additionally, it describes a variety of neural network architectures under a unifying theory, allowing readers to better comprehend the variety of neural network algorithms.

This book presents an overview of the state of the art in the application of unsupervised algorithms to the study of undeciphered scripts. It shows how these algorithms can be applied to the Indus script, which is a major undeciphered script of the ancient world. The author explains how modern pattern recognition and search techniques implemented on the digital computer can considerably reduce human effort in uncovering patterns and syntax in undeciphered scripts. These can then form the basis on which semantic information and decipherment can be obtained.

This book provides a practical, hands-on introduction to the use of R in machine learning. Both supervised and unsupervised learning are comprehensively covered in the book, and the book explores key topics in machine learning, including support vector machines and neural networks. The book also describes various machine learning R packages and includes example code and use cases throughout.

Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The new edition will update all chapters to RapidMiner 7, and will add at least six new chapters, including new chapters on text mining, time series, and educational data mining.