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

The Riemann problem is the most fundamental problem in the entire field of non-linear hyperbolic conservation laws. Since first posed and solved in 1860, great progress has been achieved in the one-dimensional case. However, the two-dimensional case is substantially different. Although research interest in it has lasted more than a century, it has yielded almost no analytical demonstration. It remains a great challenge for mathematicians.This volume presents work on the two-dimensional Riemann problem carried out over the last 20 years by a Chinese group. The authors explore four models: scalar conservation laws, compressible Euler equations, zero-pressure gas dynamics, and pressure-gradient equations. They use the method of generalized characteristic analysis plus numerical experiments to demonstrate the elementary field interaction patterns of shocks, rarefaction waves, and slip lines. They also discover a most interesting feature for zero-pressure gas dynamics: a new kind of elementary wave appearing in the interaction of slip lines-a weighted Dirac delta shock of the density function.  The Two-Dimensional Riemann Problem in Gas Dynamics establishes the rigorous mathematical theory of delta-shocks and Mach reflection-like patterns for zero-pressure gas dynamics, clarifies the boundaries of interaction of elementary waves, demonstrates the interesting spatial interaction of slip lines, and proposes a series of open problems. With applications ranging from engineering to astrophysics, and as the first book to examine the two-dimensional Riemann problem, this volume will prove fascinating to mathematicians and hold great interest for physicists and engineers.

The Two-Dimensional Riemann Problem in Gas Dynamics (Monographs and Surveys in Pure and Applied Mathematics)

Science & Math

Li, Jiequan

Yang, Shuli

Mathematics

Chapman and Hall/CRC

The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning). Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms. This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning.

Mathematical Analysis of Machine Learning Algorithms

Cambridge University Press

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.

Weiss, Sholom M. M.

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 successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

高等学校艺术设计类专业"十二五"规划教材•创意大师产学融合系列丛书:设计思维与方法

Shanghai Jiaotong University Press

See the best price to sell, buy, or rent books by Zhang, Tong. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Zhang, Tong

See the best price to sell, buy, or rent books by Zhang, Tong. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

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

Text Mining: Predictive Methods for Analyzing Unstructured Information

The Two-Dimensional Riemann Problem in Gas Dynamics (Monographs and Surveys in Pure and Applied Mathematics)

Mathematical Analysis of Machine Learning Algorithms

Text Mining: Predictive Methods for Analyzing Unstructured Information

Fundamentals of Predictive Text Mining (Texts in Computer Science)

Fundamentals of Predictive Text Mining (Texts in Computer Science)

高等学校艺术设计类专业"十二五"规划教材•创意大师产学融合系列丛书:设计思维与方法