Описание: This book formalizes multiple abstraction (arranging knowledge hierarchically using more than one hierarchy) based on graphs, and studies in-depth its implications in path search, graph isomorphism, and in the automatic construction of multi-hierarchical structures. It also describes its application as a model of the large-scale space environment of mobile robots, including real mobile robots and a well-known computational model of the human cognitive map: the Spatial Semantic Hierarchy. This book is intended for PhD students and in general for robotics, computer science, and artificial intelligence researchers.
Описание: A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution* abundance models based on many sampling protocols, including distance sampling* capture-recapture models with individual effects* spatial capture-recapture models based on camera trapping and related methods* population and metapopulation dynamic models* models of biodiversity, community structure and dynamics
Описание: New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.
Описание: This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms, by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search, motivates and describes BOA and hBOA in a way accessible to a wide audience, and presents numerous results confirming that they are revolutionary approaches to black-box optimization.
Автор: Marc Kery Название: Applied Hierarchical Modeling in Ecology ISBN: 0128013788 ISBN-13(EAN): 9780128013786 Издательство: Elsevier Science Рейтинг: Цена: 5975 р. 6639.00-10% Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: . Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. . Thisfirstvolumeexplainsstatic models/procedures in the context of hierarchical modelsthat collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields.
Описание: Humans are remarkable in processing speech, audio, image and some biomedical signals. Artificial neural networks are proved to be successful in performing several cognitive, industrial and scientific tasks. This book surveys on the applications of artificial neural networks in the areas of speech, audio, image and biomedical signal processing.
Автор: Caelli Название: Machine Learning and Image Interpretation ISBN: 030645761X ISBN-13(EAN): 9780306457616 Издательство: Springer Рейтинг: Цена: 20097 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presented in a book and a CD pack, this semi-autobiographical title recounts the many adventures and mishaps of George (George Wingrave), Harris (Carl Hentschel), J. (the author and narrator) and his remarkable dog, Montmorency, during a boating trip on the Thames in the late 19th century.
Автор: Kopparapu Sunil K., Desai Uday B. Название: Bayesian Approach to Image Interpretation ISBN: 0792373723 ISBN-13(EAN): 9780792373728 Издательство: Springer Рейтинг: Цена: 14955 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas. For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial. For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable. New ideas introduced in the book include: New approach to image interpretation using synergism between the segmentation and the interpretation modules. A new segmentation algorithm based on multiresolution analysis. Novel use of the Bayesian networks (causal networks) for image interpretation. Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework. Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition.
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