Ротштейн О.П. Soft computing в біотехнології: багатофакторний аналіз і діагностика

«А.Тьюринг. Может ли машина мыслить? (С приложением статьи Дж. фон Неймана Общая и логическая теория автоматов». Пер. и примечания Ю.В. Данилова)»: ГИФМЛ; М.; 1960 «Может ли машина мыслить?» – едва ли не самая знаменитая статья А. Тьюринга. Даже сейчас, спустя почти 60 лет после ее написания, она, вызвавшая в свое время огромное количество как серьезных исследований, так и псевдонаучных спекуляций, нисколько не утеряла своего значения. Статья на...

Издательство Springer, 2011, -172 pp. Simplicity in nature is the ultimate sophistication. Honey bees are not able to play chess or solve the Tower of Hanoi puzzle; however, they do know how to build, defend, forage, navigate, and communicate for survival. They can even learn to recognize human letters independent of size, color, position, or font. Instinct is an inherited behavior that responds to particular environmental stimuli. In his book O...

This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and linear algebra as taught in a rst- or secondyear undergraduate course on mathematics for scientists and engineers.

Издательство InTech, 2011, -234 pp. Communication and the ability to interact with the environment are basic human needs. Millions of people worldwide suff er from such severe physical disabilities that they cannot even meet these basic needs. Even though they may have no motor mobility, however, the sensory and cognitive functions of the physically disabled are usually intact. This makes them good candidates for Brain Computer Interface (BCI) t...

Издательство Springer, 1998, -480 pp. Managing uncertainty is one of the key questions in a diverse range of areas in computing. Many researchers in both universities and commercial organizations are seeking better information on applying uncertainty formalisms. There is a particular need for analyses comparing and contrasting different approaches to uncertainty formalisms and we hope that the papers in this book help to fill this need. The pap...

Из серии Foundations and Trends in Machine Learning издательства NOWPress, 2008, -163 pp. This paper describes a novel machine learning framework for solving sequential decision problems called Markov decision processes (MDPs) by iteratively computing low-dimensional representations and approximately optimal policies. A unified mathematical framework for learning representation and optimal control in MDPs is presented based on a class of singula...

Издательство Springer, 1997, -310 pp. Over the last several years, there has been a distinct flurry of research and development activity under the 'agents' banner in universities and research laboratories world-wide. It is worrying that agent technology is being promulgated by some as a panacea to a whole host of problems. Rampant use and misuse of the word 'agent', exaggerated claims and overselling of this nascent technology are giving currenc...

Издательство Addison Wesley, 2005, -331 pp. Artificial intelligence (AI) has been in existence almost as long as computers. However, only recently have AI techniques been widely incorporated by companies to enhance traditional business applications. Building Intelligent .NET Applications is an introduction to the world of Artificial Intelligence (AI) for .NET programmers. It is the first book to show professional .NET developers how to incorpora...

625 pages 2010 by Taylor and Francis Group, LLC Rapid advancements in the application of soft computing tools and techniques have proven valuable in the development of highly scalable systems and resulted in brilliant applications, including those in biometric identification, interactive voice response systems, and data mining. Although many resources on the subject adequately cover the theoretic concepts, few provide clear insight into practic...

Из серии Foundations and Trends in Machine Learning издательства NOWPress, 2008, -305 pp. The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statist...