Artificial Intelligence By Janakiraman Ebook

This compact and easy-to-read book describes in detail the basic principles of Decision Support Systems (DSS). The book also gives a comprehensive account of the various models used in decision making process, the many facets of DSS, and explains how they are implemented. Further, it discusses the significance of business reengineering, the role of client-server technology, Internet and Intranet, and analyzes the concepts of Database Management Systems (DBMS), model management and various GUIs.Designed as a textbook for the undergraduate and postgraduate students of Computer Science and Management, this book would also be of considerable assistance to the practising professional. JANAKIRAMAN, V. V.S.JANAKIRAMAN, PH.D. Professor of Computer Science, PSG College of Arts and Commerce, Coimbatore. An industrial information technology consultant to a number of companies, Professor Janakiraman is a life member of Indian Society for Technical Education (ISTE).

He has published a number of articles in reputed national and international journals and has co-authored a book on AI and Expert Systems SARUKESI, K. SARUKESI, PH.D. Professor of Computer Science, Bharathiyar University, Coimbatore. He is a co-author of the book on AI and Expert Systems.

Artificial intelligence emphasizing the most successful area of practical applications, knowledge based expert computer systems. Chertyozh grindera. - to define, clarify, and make. Kiz uzatuga tilekter. Download Free eBook:Artificial Intelligence - Free chm, pdf ebooks download. Artificial Intelligence: With an Introduction to Machine Learning, Second Edition.

› Author by: Doug Fisher Language: en Publisher by: Springer Science & Business Media Format Available: PDF, ePub, Mobi Total Read: 22 Total Download: 984 File Size: 49,8 Mb Description: Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others.