Search (11 results, page 1 of 1)

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Classification

Mat T 1091 / Neuronales Netz

SBB

Mat T 1091 / Neuronales Netz

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Classification

Mat T 1091 / Künstliche Intelligenz

Footnote

Rez. in: Spektrum der Wissenschaft 1998, H.3, S.108-110 (T. Scheuer)

SBB

Mat T 1091 / Künstliche Intelligenz

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Classification

Mat T 1091 / Neuronales Netz

SBB

Mat T 1091 / Neuronales Netz

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Abstract

Ontologies have been developed and investigated for quite a while now in artificial intelligente and natural language processing to facilitate knowledge sharing and reuse. More recently, the notion of ontologies has attracied attention from fields such as intelligent information integration, cooperative information systems, information retrieval, electronic commerce, and knowledge management. The author systematicaliy introduces the notion of ontologies to the non-expert reader and demonstrates in detail how to apply this conceptual framework for improved intranet retrieval of corporate information and knowledge and for enhanced Internet-based electronic commerce. In the second part of the book, the author presents a more technical view an emerging Web standards, like XML, RDF, XSL-T, or XQL, allowing for structural and semantic modeling and description of data and information.

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Content

Enthält auch die Beiträge: BROOKS, R.A.: Künstliche Intelligenz und Roboterentwicklung - RITTER, H.: Die Evolution der künstlichen Intelligenz - ENGEL, A.K. u. W. SINGER: Neuronale Grundlagen des Bewußtseins - METZINGER, T.: Postbiotisches Bewußtsein: wie man ein künstliches Subjekt baut - und warum wir es nicht tun sollten - HARA, F.: Gesichtsrobotik und emotional computing

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Date

23. 3.2008 19:10:22

Footnote

In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.

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Content

Inhalt: Definitionen von Knowledge, Knowledge Management und Open Source.- Vision eines Knowledge Management- (KM-) Systems.- Vorhandene Open-Source-Basis.- Technische Basis.- Start mit einem Groupware System.- Alternativ Start mit einem Content-Management-System.- Einbinden von Groupware oder CMS bzw. Erweitern um DMS.- Weiterer Ausbau.- Zusammenfassungen, Abschluss und Ausblick.- Literaturverzeichnis.- Index.

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Date

21. 3.2008 19:10:22

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Classification

006.7 22

Date

7. 3.2007 19:30:22

DDC

006.7 22