Data mining and knowledge discovery (DMKD) is a rapidly expanding field in computer science. It has become very important because of an increased demand for methodologies and tools that can help the analysis and under...
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ISBN:
(数字)9781846281839
ISBN:
(纸本)9781852338671;9781447157526
Data mining and knowledge discovery (DMKD) is a rapidly expanding field in computer science. It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions like hospitals, research laboratories, banks, insurance companies, and retail stores and by Internet users. This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis [2].
Distributed search by agents is an important topic of distributed AI and has not been treated thoroughly as such. While the scope of work on multi-agent systems has grown steadily over the last decade, very little of ...
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ISBN:
(数字)9781848000407
ISBN:
(纸本)9781848000391;9781849967105
Distributed search by agents is an important topic of distributed AI and has not been treated thoroughly as such. While the scope of work on multi-agent systems has grown steadily over the last decade, very little of it has spilled into distributed search. In conrast, the constraints processing community has produced a sizable body of work on distributed constrained search. Parado- cally, a community that concentrates on search algorithms and heuristics has created a distributed model for agents that cooperate on solving hard search problems. Traditionally, this ?eld has been named Ditributed Constraints S- isfaction and lately also distributed constraints optimization. The present book attempts to prompt deeper response from the MAS community and hopefully to give rise to cooperative work on distributed search by agents. In order to achieve this high goal, the book presents the large body of work on distributed search by constrained agents. The presentation emphasizes many aspects of distributed computation that connect naturally to multi-agent systems, - pecially measures of performance for distributed search algorithms and the impact of delays in communication. Distributed Constraints Satisfaction Problems (DisCSPs) have been st- ied over the last decade, starting with the pioneering proposal by Makoto Yokoo [18]. The ?rst distributed search algorithm for DisCSPs - As- chronous Backtracking (ABT) - was ?rst published in complete format in 1998 [64]. The ?rst book on Distributed Constraints Satisfaction Problems has appeared as early as 2000 [61].
Relatively new research ?elds such as ambient intelligence, intelligent envir- ments, ubiquitous computing, and wearable devices have emerged in recent years. These ?elds are related by a common theme: making use of n...
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ISBN:
(数字)9781848003460
ISBN:
(纸本)9781848003453;9781849967914
Relatively new research ?elds such as ambient intelligence, intelligent envir- ments, ubiquitous computing, and wearable devices have emerged in recent years. These ?elds are related by a common theme: making use of novel technologies to enhance user experience by providing user-centric intelligent environments, - moving computers from the desktop and making computing available anywhere and anytime. It must be said that the concept of intelligent environments is not new and beganwithhomeautomation. Thechoiceofnameforthe?eldvariessomewhatfrom continent to continent in the English-speaking world. In general intelligent space is synonymous to intelligent environments or smart spaces of which smart homes is a sub?eld. In this collection, the terms intelligent environments and ambient int- ligence are used interchangeably throughout. Such environments are made possible by permeating living spaces with intelligent technology that enhances quality of life. In particular, advances in technologies such as miniaturized sensors, advances in communication and networking technology including high-bandwidth wireless devices and the reduction in power consumption have made possible the concept of intelligent environments. Environments such as a home, an of?ce, a shopping mall, and a travel port utilize data provided by users to adapt the environment to meet the user’s needs and improve human-machine interactions. The user information is gathered either via wearable devices or by pervasive sensors or a combination of both. Intelligent environments brings together a number of research ?elds from computer science, such as arti?cial intelligence, computer vision, machine learning, and robotics as well as engineering and architecture.
A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challen...
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ISBN:
(数字)9783030007348
ISBN:
(纸本)9783030007331
A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning.;covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
On the surface, information is a relatively straightforward and intuitive concept. Underneath, however, information is a relatively versatile and mysterious entity. For instance, the way a physicist looks at informati...
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ISBN:
(数字)9783319590905
ISBN:
(纸本)9783319590899;9783319865454
On the surface, information is a relatively straightforward and intuitive concept. Underneath, however, information is a relatively versatile and mysterious entity. For instance, the way a physicist looks at information is not necessarily the same way as that of a biologist, a neuroscientist, a computer scientist, or a philosopher. Actually, when it comes to information, it is common that each field has its domain specific views, motivations, interpretations, definitions, methods, technologies, and challenges.
This work summarizes the theoretical and algorithmic basis of optimized pr- abilistic advising. It developed from a series of targeted research projects s- ported both by the European Commission and Czech grant bodies...
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ISBN:
(数字)9781846282546
ISBN:
(纸本)9781852339289;9781447156758
This work summarizes the theoretical and algorithmic basis of optimized pr- abilistic advising. It developed from a series of targeted research projects s- ported both by the European Commission and Czech grant bodies. The source text has served as a common basis of communication for the research team. When accumulating and re?ning the material we found that the text could also serve as • a grand example of the strength of dynamic Bayesian decision making, • a practical demonstration that computational aspects do matter, • a reference to ready particular solutions in learning and optimization of decision-making strategies, • a source of open and challenging problems for postgraduate students, young as well as experienced researchers, • a departure point for a further systematic development of advanced op- mized advisory systems, for instance, in multiple participant setting. These observations have inspired us to prepare this book. Prague, Czech Republic Miroslav K´ arn´ y October 2004 Josef B¨ ohm Tatiana V. Guy Ladislav Jirsa Ivan Nagy Petr Nedoma Ludv´ ?k Tesa? r Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 2 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. 2. 1 Operator supports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. 2. 2 Mainstream multivariate techniques . . . . . . . . . . . . . . . . . 4 1. 2. 3 Probabilistic dynamic optimized decision-making . . . . . . 6 1. 3 Developed advising and its role in computer support . . . . . . . . . 6 1. 4 Presentation style, readership andlayout . . . . . . . . . . . . . . . . . . . 7 1. 5 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Underlying theory . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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