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检索条件"丛书名=Intelligent Systems Reference Library"

共 291 条记录，以下是131-140 订阅

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Approximate Reasoning by Parts: An Introduction to Rough Mereology 1

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丛书名： intelligent systems reference library

2011年

作者： Lech Polkowski (auth.)

ISBN: (数字)9783642222795

ISBN: (纸本)3642222781;9783642222788

The monograph offers a view on Rough Mereology, a tool for reasoning under uncertainty, which goes back to Mereology, formulated in terms of parts by Lesniewski, and borrows from Fuzzy Set Theory and Rough Set Theory ideas of the containment to a degree. The result is a theory based on the notion of a part to a degree. One can invoke here a formula Rough: Rough Mereology : Mereology = Fuzzy Set Theory : Set Theory. As with Mereology, Rough Mereology finds important applications in problems of Spatial Reasoning, illustrated in this monograph with examples from Behavioral Robotics. Due to its involvement with concepts, Rough Mereology offers new approaches to Granular Computing, Classifier and Decision Synthesis, Logics for Information systems, and are--formulation of well--known ideas of Neural Networks and Many Agent systems. All these approaches are discussed in this monograph. To make the exposition self--contained, underlying notions of Set Theory, Topology, and Deductive and Reductive Reasoning with emphasis on Rough and Fuzzy Set Theories along with a thorough exposition of Mereology both in Lesniewski and Whitehead--Leonard--Goodman--Clarke versions are discussed at length. It is hoped that the monograph offers researchers in various areas of Artificial Intelligence a new tool to deal with analysis of relations among concepts.

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intelligent systems 2011

引用

丛书名： intelligent systems reference library

2011年

作者： Crina Grosan Ajith Abraham

ISBN: (数字)9783642210044

ISBN: (纸本)9783642210037

Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.

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Soft Computing in Economics and Finance 2011

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丛书名： intelligent systems reference library

2011年

作者： Ludmila Dymowa

ISBN: (数字)9783642177194

ISBN: (纸本)9783642177187

Currently the methods of Soft Computing are successfully used for risk analysis in: budgeting, e-commerce development, portfolio selection, Black-Scholes option pricing models, corporate acquisition systems, evaluating investments in advanced manufacturing technology, interactive fuzzy interval reasoning for smart web shopping, fuzzy scheduling and *** essential feature of economic and financial problems it that there are always at least two criteria to be taken into account: profit maximization and risk minimization. Therefore, the economic and financial problems are multiple criteria ones. In this book, a new systematization of the problems of multiple criteria decision making is proposed which allows the author to reveal unsolved problems. The solutions of them are presented as well and implemented to deal with some important real-world problems such as investment projects evaluation, tool steel material selection problem, stock screening and fuzzy logistic. It is well known that the best results in real -world applications can be obtained using the synthesis of modern methods of soft computing. Therefore, the developed by the author new approach to building effective stock trading systems, based on the synthesis of fuzzy logic and the Dempster-Shafer theory, seems to be a considerable contribution to the application of soft computing method in economics and *** important problem of capital budgeting is the fuzzy evaluation of the Internal Rate of Return. In this book, this problem is solved using a new method which makes it possible to solve linear and nonlinear interval and fuzzy equations and systems of them. The developed new method allows the author to obtain an effective solution of the Leontjevs input-output problem in the interval setting.

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Knowledge Annotation: Making Implicit Knowledge Explicit 2011

Knowledge Annotation: Making Implicit Knowledge Explicit

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丛书名： intelligent systems reference library

2011年

作者： Alexiei Dingli

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Data Mining: Foundations and intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects 2012

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丛书名： intelligent systems reference library volume 24

2011年

作者： Dawn E. Holmes Lakhmi C. Jain (editors)

ISBN: (纸本)364223240X;9783642232404

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled DATA MINING: Foundations and intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

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Developments and Challenges for Autonomous Unmanned Vehicles 2010

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丛书名： intelligent systems reference library

2010年

作者： Anthony Finn Steve Scheding

ISBN: (数字)9783642107047

ISBN: (纸本)9783642107030

It is widely anticipated that autonomous vehicles will have a transformational impact on military forces and will play a key role in many future force structures. As a result, many tasks have already been identified that unmanned systems could undertake more readily than humans. However, for this to occur, such systems will need to be agile, versatile, persistent, reliable, survivable and lethal. This will require many of the vehicles cognitive or higher order functions to be more fully developed, whereas to date only the component or physical functions have been successfully automated and deployed. The book draws upon a broad range of others work with a view to providing a product that is greater than the sum of its parts. The discussion is intentionally approached from the perspective of improving understanding rather than providing solutions or drawing firm conclusions. Consequently, researchers reading this book with the hope of uncovering some novel theory or approach to automating an unmanned vehicle will be as disappointed as the capability planner who anticipates a catalogue of technical risks and feasibility options against his favoured list of component technologies and potential applications. Nevertheless, it is hoped that both will at least learn something of the others world and that progress will ensue as a result. For the defence policy and decision maker, this is a "must-read" book which brings together an important technology summary with a considered analysis of future doctrinal, legal and ethical issues in unmanned and autonomous systems. For research engineers and developers of robotics, this book provides a unique perspective on the implications and consequences of our craft; connecting what we do to the deployment and use of the technology in current and future defence *** Hugh Durrant-Whyte

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Towards intelligent Modeling: Statistical Approximation Theory 2011

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丛书名： intelligent systems reference library

2011年

作者： George A. Anastassiou Oktay Duman

ISBN: (数字)9783642198267

ISBN: (纸本)9783642198250

The main idea of statistical convergence is to demand convergence only for a majority of elements of a sequence. This method of convergence has been investigated in many fundamental areas of mathematics such as: measure theory, approximation theory, fuzzy logic theory, summability theory, and so on. In this monograph we consider this concept in approximating a function by linear operators, especially when the classical limit fails. The results of this book not only cover the classical and statistical approximation theory, but also are applied in the fuzzy logic via the fuzzy-valued operators. The authors in particular treat the important Korovkin approximation theory of positive linear operators in statistical and fuzzy sense. They also present various statistical approximation theorems for some specific real and complex-valued linear operators that are not positive. This is the first monograph in Statistical Approximation Theory and Fuzziness. The chapters are self-contained and several advanced courses can be *** research findings will be useful in various applications including applied and computational mathematics, stochastics, engineering, artificial intelligence, vision and machine learning. This monograph is directed to graduate students, researchers, practitioners and professors of all disciplines.

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From Curve Fitting to Machine Learning 2011

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丛书名： intelligent systems reference library

2011年

作者： Achim Zielesny

ISBN: (数字)9783642212802

ISBN: (纸本)9783642212796

The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. These sections may be skipped without affecting the main road but they will open up possibly interesting insights beyond the mere data massage. All topics are completely demonstrated with the aid of the commercial computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source so the detailed code of every method is freely accessible. All examples and applications shown throughout the book may be used and customized by the reader without any restrictions. The target readerships are students of (computer) science and engineering as well as sci

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Data Mining

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丛书名： intelligent systems reference library,,intelligent systems reference library

2011年

作者： Florin Gorunescu

ISBN: (纸本)9783642197208

"The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the 'natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since "knowledge is power". The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information."--Back cover.

关键词： Artificial intelligence. Computational intelligence. Engineering. Engineering. Data Mining. Computational intelligence. Artificial intelligence. Data mining. Data mining. Data Mining. swd fast fast gnd fast fast

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Computational Intelligence 2009

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丛书名： intelligent systems reference library

2009年

作者： Christine L. Mumford

ISBN: (数字)9783642017995

ISBN: (纸本)9783642017988

This book is the first in a new series entitled "intelligent systems reference library". It is a collection of chapters written by leading experts, covering a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence (CI). Authors in this collection recognize the limitations of individual paradigms, and propose some practical and novel ways in which different CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful problem-solving environments. Common themes to be found in the various chapters of this collection include the following: Fusion, Collaboration, and Emergence. Fusion describes the hybridization of two or more techniques, at least one of which will involve CI. Collaboration ensures that the different techniques work effectively together. Finally, Emergence refers to the phenomenon that complex behaviour can arise as a result of collaboration between simple processing elements. The book covers a wide range of cutting edge techniques and applications, and is divided into the following parts: I. Introduction II. Fusing evolutionary algorithms and fuzzy logic III. Adaptive solution schemes IV. Multi-agent systems V. Computer vision VI. Communication for CI systems VII. Artificial immune systems VIII. Parallel evolutionary algorithms IX. CI for clustering and classification

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