Kagan Eugene Probabilistic Search for Tracking Targets. Theory and Modern Applications

Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space. Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening. Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory. The authors: Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target. Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.

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David Amerland SEO Help. 20 Semantic Search Steps that Will Help Your Business Grow

Search has changed. Search marketing now works differently.Semantic search is disrupting many of the practices associated with traditional search engine optimization techniques. David Amerland takes the complex subject of semantic search optimization and reduces it into twenty manageable steps anyone can apply. Using some of the most advanced semantic SEO techniques, in plain English, he shows you how you can: Make quick SEO gains for your website Take your search marketing to the next level Use search in your branding efforts Define the uniqueness of your business Create trust and authenticity on the web Make use of current promotional techniques Integrate video and images in search marketing Shape your digital identity Form clear brand values Future-proof your SEO Start using semantic search techniques from the very first moment.Designed for the busy webmaster, business leader and entrepreneur, each chapter can be read alone, or in sequence. A comprehensive list of 200 questions, presented at the end of each chapter in tranches of ten allows you to reshape the entire way you approach digital marketing and search, irrespective of the size of your business or the budget available. Immensely practical, easy to read with each chapter standing alone this is the down to Earth aid to semantic search that you have been looking for.

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Дайвинг клуб в Москве: Дайвинг центр RuDIVE

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