Technological advances are revolutionizing computers and networks to support digital video and audio, leading to new design spaces in computer systems and applications. Under the surface of exciting multimedia technologies lies a mine of research problems. This volume presents the proceedings of an international workshop which brought together the leading researchers in all aspects of multimedia computing, communication, storage, and applications. The field of multimedia has witnessed an explosive growth in the last few years and the selection of papers for this workshop was extremely competitive. The volume contains 26 full papers and 14 short papers selected from 128 contributions, organized into parts on: network and operating system support for multimedia; multimedia on-demand services; media synchronization; distributed multimedia systems; network and operating system support for multimedia; multimedia models, frameworks, and document architectures; and multimedia workstations and platforms.

Text Mining: Predictive Methods for Analyzing Unstructured Information (Lecture Notes in Computer Science)

Computers & Technology

Weiss, Sholom M.

Indurkhya, Nitin

Zhang, Tong

Certification

Springer

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

Text Mining: Predictive Methods for Analyzing Unstructured Information

Damerau, Fred

Computer Science

This book offers a practical introduction to expert systems and is designed not only for computer programmers but for all those who want to know how expert systems are structured and what they can do.

A Practical Guide to Designing Expert Systems

Kulikowski, Casimir A.

Rowman & Littlefield Publishers

Fundamentals of Predictive Text Mining (Texts in Computer Science)

This is an introductory textbook and guide to the rapidly evolving field of predictive text mining. There are chapter summaries, historical and bibliographic remarks, and classroom-tested exercises for each chapter. Descriptive case studies are also included.

This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans.
 Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests.
 The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.

Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems

Morgan Kaufmann

The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles―and their practical manifestations―in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses. Note: If you already own Predictive Data Mining: A Practical Guide, please see ISBN 1-55860-477-4 to order the accompanying software. To order the book/software package, please see ISBN 1-55860-478-2.

Predictive Data Mining: A Practical Guide (The Morgan Kaufmann Series in Data Management Systems)

The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles and their practical manifestations in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses. 
A state-of-the-art data-mining software kit accompanies the book. The software, which is delivered through a special web site, is a collection of routines for efficient mining of big data. Both classical and the more computationally expensive state-of-the-art prediction methods are included. Using a standard spreadsheet data format, this kit implements all of the data-mining tasks described in the book. The software is available for Windows 95/NT and Unix platforms (no need to specify when ordering). * Focuses on the preparation and organization of data and the development of an overall strategy for data mining. * Reviews sophisticated prediction methods that search for patterns in big data. * Describes how to accurately estimate future performance of proposed solutions. * Illustrates the data-mining process and its potential pitfalls through real-life case studies.

Predictive Data Mining: A Practical Guide (with Software)

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