Автор: Liu Zhi-Qiang, Cai Jin-Hai, Buse Richard Название: Handwriting Recognition / Soft Computing and Probabilistic Approaches ISBN: 3540401776 ISBN-13(EAN): 9783540401773 Издательство: Springer Рейтинг: Цена: 16169 р. Наличие на складе: Есть (1 шт.) Описание: This book takes a fresh look at the problem of unconstrained handwriting recognition and introduces the reader to new techniques for the recognition of written words and characters using statistical and soft computing approaches. The types of uncertainties and variations present in handwriting data are discussed in detail. The book presents several algorithms that use modified hidden Markov models and Markov random field models to simulate the handwriting data statistically and structurally in a single framework. The book explores methods that use fuzzy logic and fuzzy sets for handwriting recognition. The effectiveness of these techniques is demonstrated through extensive experimental results and real handwritten characters and words.

Автор: Coletti G., Scozzafava R. Название: Probabilistic Logic in a Coherent Setting ISBN: 1402009178 ISBN-13(EAN): 9781402009174 Издательство: Springer Рейтинг: Цена: 13859 р. Наличие на складе: Поставка под заказ.

Описание: The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability. It is a `flexible' and unifying tool suited for handling, e.g., partial probability assessments (not requiring that the set of all possible `outcomes' be endowed with a previously given algebraic structure, such as a Boolean algebra), and conditional independence, in a way that avoids all the inconsistencies related to logical dependence (so that a theory referring to graphical models more general than those usually considered in bayesian networks can be derived). Moreover, it is possible to encompass other approaches to uncertain reasoning, such as fuzziness, possibility functions, and default reasoning. The book is kept self-contained, provided the reader is familiar with the elementary aspects of propositional calculus, linear algebra, and analysis.

Автор: Luc De Raedt; Paolo Frasconi; Kristian Kersting; S Название: Probabilistic Inductive Logic Programming ISBN: 3540786511 ISBN-13(EAN): 9783540786511 Издательство: Springer Рейтинг: Цена: 8084 р. Наличие на складе: Поставка под заказ.

Описание: One of the key open questions within arti?cial intelligence is how to combine probability and logic with learning. This question is getting an increased - tentioninseveraldisciplinessuchasknowledgerepresentation, reasoningabout uncertainty, data mining, and machine learning simulateously, resulting in the newlyemergingsub?eldknownasstatisticalrelationallearningandprobabil- ticinductivelogicprogramming.Amajordriving forceisthe explosivegrowth in the amount of heterogeneous data that is being collected in the business and scienti?c world. Example domains include bioinformatics, chemoinform- ics, transportation systems, communication networks, social network analysis, linkanalysis, robotics, amongothers.Thestructuresencounteredcanbeass- pleassequencesandtrees(suchasthosearisinginproteinsecondarystructure predictionandnaturallanguageparsing)orascomplexascitationgraphs, the WorldWideWeb, andrelationaldatabases. This book providesan introduction to this ?eld with an emphasison those methods based on logic programming principles. The book is also the main resultofthesuccessfulEuropeanISTFETprojectno.FP6-508861onAppli- tionofProbabilisticInductiveLogicProgramming(APRILII,2004-2007).This projectwascoordinatedbytheAlbertLudwigsUniversityofFreiburg(Germany, Luc De Raedt) and the partners were Imperial College London (UK, Stephen MuggletonandMichaelSternberg), theHelsinkiInstituteofInformationTe- nology(Finland, HeikkiMannila), theUniversit adegliStudidiFlorence(Italy, PaoloFrasconi), andtheInstitutNationaldeRechercheenInformatiqueet- tomatiqueRocquencourt(France, FrancoisFages).Itwasconcernedwiththeory, implementationsandapplicationsofprobabilisticinductivelogicprogramming. Thisstructureisalsore?ectedinthebook. The book starts with an introductory chapter to "Probabilistic Inductive LogicProgramming"byDeRaedtandKersting.Inasecondpart, itprovidesa detailedoverviewofthemostimportantprobabilisticlogiclearningformalisms and systems. We are very pleased and proud that the scientists behind the key probabilistic inductive logic programming systems (also those developed outside the APRIL project) have kindly contributed a chapter providing an overviewoftheircontributions.Thisincludes: relationalsequencelearningte- niques (Kersting et al.), using kernels with logical representations (Frasconi andPasserini), MarkovLogic(Domingosetal.), the PRISMsystem (Satoand Kameya), CLP(BN)(SantosCostaetal.), BayesianLogicPrograms(Kersting andDeRaedt), andtheIndependentChoiceLogic(Poole).Thethirdpartthen provides a detailed account of some show-caseapplications of probabilistic - ductive logic programming, more speci?cally: in protein fold discovery (Chen et al.), haplotyping (Landwehr and Mielik] ainen) and systems biology (Fages andSoliman). The ?nal parttouchesupon sometheoreticalinvestigationsand VI Preface includes chaptersonbehavioralcomparisonof probabilisticlogicprogramming representations(MuggletonandChen)andamodel-theoreticexpressivityan- ysis(Jaeger).

Описание: Uncertainty in key parameters within a chip and between different chips in the deep sub micron area plays a more and more important role. As a result, manufacturing process spreads need to be considered during the design process. Quantitative methodology is needed to ensure faultless functionality, despite existing process variations within given bounds, during product development. This book presents the technological, physical, and mathematical fundamentals for a design paradigm shift, from a deterministic process to a probability-orientated design process for microelectronic circuits. Readers will learn to evaluate the different sources of variations in the design flow in order to establish different design variants, while applying appropriate methods and tools to evaluate and optimize their design.

Описание: Probabilistic and Statistical Methods in Computer Science presents a large variety of applications of probability theory and statistics in computer science and more precisely in algorithm analysis, speech recognition and robotics. It is written on a self-contained basis: all probabilistic and statistical tools needed are introduced on a comprehensible level. In addition all examples are worked out completely. Most of the material is scattered throughout available literature. However, this is the first volume that brings together all of this material in such an accessible format. Probabilistic and Statistical Methods in Computer Science is intended for students in computer science and applied mathematics, for engineers and for all researchers interested in applications of probability theory and statistics. It is suitable for self study as well as being appropriate for a course or seminar.

Описание: The book systematically provides the reader with a broad range of systems/research work to date that addresses the importance of combining numerical and symbolic approaches to reasoning under uncertainty in complex applications. It covers techniques on how to extend propositional logic to a probabilistic one and compares such derived probabilistic logic with closely related mechanisms, namely evidence theory, assumption-based truth maintenance systems and rough sets, in terms of representing and reasoning with knowledge and evidence.The book primarily addresses researchers, practitioners, students and lecturers in the field of Artificial Intelligence, particularly in the areas of reasoning under uncertainty, logic, knowledge representation and reasoning, and non-monotonic reasoning.

Описание: This book focuses like a laser beam on one of the hottest topics in evolutionary computation over the last decade or so: estimation of distribution algorithms (EDAs). EDAs are an important current technique that is leading to breakthroughs in genetic and evolutionary computation and in optimization more generally. I'm putting Scalable Optimization via Probabilistic Modeling in a prominent place in my library, and I urge you to do so as well. This volume summarizes the state of the art at the same time it points to where that art is going. Buy it, read it, and take its lessons to heart.This book is an excellent compilation of carefully selected topics in estimation of distribution algorithms---search algorithms that combine ideas from evolutionary algorithms and machine learning. The book covers a broad spectrum of important subjects ranging from design of robust and scalable optimization algorithms to efficiency enhancements and applications of these algorithms. The book should be of interest to theoreticians and practitioners alike, and is a must-have resource for those interested in stochastic optimization in general, and genetic and evolutionary algorithms in particular.This edited book portrays population-based optimization algorithms and applications, covering the entire gamut of optimization problems having single and multiple objectives, discrete and continuous variables, serial and parallel computations, and simple and complex function models. Anyone interested in population-based optimization methods, either knowingly or unknowingly, use some form of an estimation of distribution algorithm (EDA). This book is an eye-opener and a must-read text, covering easy-to-read yet erudite articles on established and emerging EDA methodologies from real experts in the field.This book is an excellent comprehensive resource on estimation of distribution algorithms. It can serve as the primary EDA resource for practitioner or researcher. The book includes chapters from all major contributors to EDA state-of-the-art and covers the spectrum from EDA design to applications. These algorithms strategically combine the advantages of genetic and evolutionary computation with the advantages of statistical, model building machine learning techniques. EDAs are useful to solve classes of difficult real-world problems in a robust and scalable manner.Machine-learning methods continue to stir the public's imagination due to its futuristic implications. But, probability-based optimization methods can have great impact now on many scientific multiscale and engineering design problems, especially true with use of efficient and competent genetic algorithms (GA) which are the basis of the present volume. Even though efficient and competent GAs outperform standard techniques and prevent negative issues, such as solution stagnation, inherent in the older but more well-known GAs, they remain less known or embraced in the scientific and engineering communities. To that end, the editors have brought together a selection of experts that (1) introduce the current methodology and lexicography of the field with illustrative discussions and highly useful references, (2) exemplify these new techniques that dramatic improve performance in provable hard problems, and (3) provide real-world applications of these techniques, such as antenna design. As one who has strayed into the use of genetic algorithms and genetic programming for multiscale modeling in materials science, I can say it would have been personally more useful if this would have come out five years ago, but, for my students, it will be a boon.The Factorized Distribution Algorithm and the Minimum Relative Entropy Principle.- Linkage Learning via Probabilistic Modeling in the ECGA.- Hierarchical Bayesian Optimization Algorithm.- Numerical Optimization with RealвЂ“Valued EstimationвЂ“ofвЂ“ Distribution Algorithms.- A Survey of Probabilistic Model Building Genetic Programming.- Efficiency Enhancement of Estimation of Distribution Algorithms.- Design of Parallel Estimation of Distribution Algorithms.- Incorporating a priori Knowledge in Probabilistic-Model Based Optimization.- Multiobjective Estimation of Distribution Algorithms.- Effective and Reliable Online Classification Combining XCS with EDA Mechanisms.- Military Antenna Design Using a Simple Genetic Algorithm and hBOA.- Feature Subset Selection with Hybrids of Filters and Evolutionary Algorithms.- BOA for Nurse Scheduling.- Searching for Ground States of Ising Spin Glasses with Hierarchical BOA and Cluster Exact Approximation.

Автор: Coletti G., Scozzafava R. Название: Probabilistic Logic in a Coherent Setting ISBN: 1402009704 ISBN-13(EAN): 9781402009709 Издательство: Springer Рейтинг: Цена: 9816 р. Наличие на складе: Поставка под заказ.

Описание: The approach to probability theory followed in this book (which differs radically from the usual one, based on a measure-theoretic framework) characterizes probability as a linear operator rather than as a measure, and is based on the concept of coherence, which can be framed in the most general view of conditional probability. It is a `flexible' and unifying tool suited for handling, e.g., partial probability assessments (not requiring that the set of all possible `outcomes' be endowed with a previously given algebraic structure, such as a Boolean algebra), and conditional independence, in a way that avoids all the inconsistencies related to logical dependence (so that a theory referring to graphical models more general than those usually considered in bayesian networks can be derived). Moreover, it is possible to encompass other approaches to uncertain reasoning, such as fuzziness, possibility functions, and default reasoning. The book is kept self-contained, provided the reader is familiar with the elementary aspects of propositional calculus, linear algebra, and analysis.

Автор: Studeny Milan Название: Probabilistic Conditional Independence Structures ISBN: 1852338911 ISBN-13(EAN): 9781852338916 Издательство: Springer Рейтинг: Цена: 16169 р. Наличие на складе: Поставка под заказ.

Описание: Conditional independence is a topic that lies between statistics and artificial intelligence. This title provides the mathematical description of probabilistic conditional independence structures. It presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets.

Описание: In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;contributions to the area are coming from computer science, mathematics, statistics and engineering.This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.

Автор: Vidyasagar Название: Probabilistic Methods in Cancer Biology ISBN: 1447147502 ISBN-13(EAN): 9781447147503 Издательство: Springer Рейтинг: Цена: 4614 р. Наличие на складе: Поставка под заказ.

Описание: This brief introduces people with a basic background in probability theory to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics.

Автор: Kramosil Ivan Название: Probabilistic Analysis of Belief Functions ISBN: 030646702X ISBN-13(EAN): 9780306467028 Издательство: Springer Рейтинг: Цена: 16169 р. Наличие на складе: Поставка под заказ.

Описание: This volume is a highly theoretical and mathematical study analyzing the notion and theory of belief functions, also known as the Dempster-Shafer theory, from the point of view of the classical Kolmogorov axiomatic probability theory. In other terms, the theory of belief functions is taken as an interesting, non-traditional application of probability theory, and the standard methodology of probability theory, and measure theory in general, is applied in order to arrive at some new and perhaps interesting generalizations and results not accessible within the classical combinatorial framework of the theory of belief functions (Dempster-Shafer theory) over finite spaces. The relation to great systems and their theory seems to be very close and should become clear from the first two chapters of the book.

ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru