Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science)

You looking to find the "Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science)" Good news! You can purchase Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science) with secure price and compare to view update price on this product. And deals on this product is available only for limited time.

   

Product Description

Knowledge discovery is an area of computer science that attempts to uncover interesting and useful patterns in data that permit a computer to perform a task autonomously or assist a human in performing a task more efficiently.
Soft Computing for Knowledge Discovery provides a self-contained and systematic exposition of the key theory and algorithms that form the core of knowledge discovery from a soft computing perspective. It focuses on knowledge representation, machine learning, and the key methodologies that make up the fabric of soft computing - fuzzy set theory, fuzzy logic, evolutionary computing, and various theories of probability (e.g. na �ve Bayes and Bayesian networks, Dempster-Shafer theory, mass assignment theory, and others). In addition to describing many state-of-the-art soft computing approaches to knowledge discovery, the author introduces Cartesian granule features and their corresponding learning algorithms as an intuitive approach to knowledge discovery. This new approach embraces the synergistic spirit of soft computing and exploits uncertainty in order to achieve tractability, transparency and generalization. Parallels are drawn between this approach and other well known approaches (such as naive Bayes and decision trees) leading to equivalences under certain conditions.
The approaches presented are further illustrated in a battery of both artificial and real-world problems. Knowledge discovery in real-world problems, such as object recognition in outdoor scenes, medical diagnosis and control, is described in detail. These case studies provide further examples of how to apply the presented concepts and algorithms to practical problems.
The author provides web page access to an online bibliography, datasets, source codes for several algorithms described in the book, and other information.
Soft Computing for Knowledge Discovery is for advanced undergraduates, professionals and researchers in computer science, engineering and business information systems who work or have an interest in the dynamic fields of knowledge discovery and soft computing.

Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science) Review

The author, James Shanahan is internationally renowned across a number of computing fields including AI, information retrieval, information access, linguistics, text/data mining and machine learning. Indeed his expertise is reflected in his already substantial and growing IP portfolio. The book quickly made it's mark leading to a substantial international speaking itinerary and it has directly influenced many new approaches, research and developments in soft computing and knowledge discovery.

The book forms a comprehensive discussion and overview of the traditional soft computing approaches to knowledge discovery and, importantly, advances the science to Cartesian granule features and their corresponding learning algorithms thus introducing an intuitive approach to knowledge discovery.

The book and it's contents are well thought out both in how it presents complexities and in how it argues and presents this new approach vis a vi the traditional and other approaches. The approach is supported with examples applied to diverse scenarios including object recognition in outdoor scenes and in medical diagnosis and control.

It's not just a book but an essential reference source, teaching tool and resource for all with an interest in the topic. It provides a host of datasets and source codes for several algorithms described in the book.

Don't stop with this book. James Shanahan continues to surprise and amaze in the computing field and can be found presenting at numerous events each year from Asia to the US to Europe. He has an avid following and is certainly `one to watch'.

Most of the consumer Reviews tell that the "Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science)" are high quality item. You can read each testimony from consumers to find out cons and pros from Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features (The Springer International Series in Engineering and Computer Science) ...