الصفحة 2
الصفحة 2
Text, Speech and Dialogue ; 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings
Constitutes the refereed proceedings of the 10th International Conference on Text, Speech and Dialogue, TSD 2007, held in Pilsen, Czech Republic, September 3-7, 2007. The 80 revised full papers presented together with 4 invited papers were carefully reviewed and selected from 198 submissions. The papers present a wealth of state-of-the-art research results in the field of natural language processing with an emphasis on text, speech, and spoken dialogue ranging from theoretical and methodological issues to applications in various fields and with special focus on corpora, texts and tra
Text Mining for Information Professionals : An Uncharted Territory
Focuses on a basic theoretical framework dealing with the problems, solutions, and applications of text mining and its various facets in a very practical form of case studies, use cases, and stories. Contains 11 chapters with 14 case studies showing 8 different text mining and visualization approaches, and 17 stories. In addition, both a website and a Github account are also maintained for the book. They contain the code, data, and notebooks for the case studies; a summary of all the stories shared by the librarians/faculty; and hyperlinks to open an interactive virtual RStudio/Jupyter Notebook environment.
Text mining : Predictive methods for analyzing unstructured information
One consequence of the pervasive use of computers is that most documents originate in digital form. Text mining—the process of searching, retrieving, and analyzing unstructured, natural-language text—is concerned with how to exploit the textual data embedded in these documents. Text Mining presents a comprehensive introduction and overview of the field, integrating related topics (such as artificial intelligence and knowledge discovery and data mining) and providing practical advice on how readers can use text-mining methods to analyze their own data. Emphasizing predictive methods, the book unifies all key areas in text mining: preprocessing, text categorization, information search and retrieval, clustering of documents, and information extraction. In addition, it identifies emerging directions for those looking to do research in the area. Some background in data mining is beneficial, but not essential.
Text Data Mining
Offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview.
Text Analytics : An introduction to the science and applications of unstructured information analysis
A concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources.
Technologies and Applications for Big Data Value
This book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas.
Support Vector Machines
Explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and their computational efficiency compared to several other methods.The book provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature. The book can thus serve as both a basis for graduate courses and an introduction for statisticians, mathematicians, and computer scientists. It further provides a valuable reference for researchers working in the field.The book covers all important topics concerning support vector machines such as: loss functions and their role in the learning process; reproducing kernel Hilbert spaces and their properties; a thorough statistical analysis that uses both traditional uniform bounds and more advanced localized techniques based on Rademacher averages and Talagrand's inequality; a detailed treatment of classification and regression; a detailed robustness analysis; and a description of some of the most recent implementation techniques.
Super-Recursive Algorithms
New discoveries about algorithms are leading scientists beyond the Church-Turing Thesis, which governs the "algorithmic universe" and asserts the conventionality of recursive algorithms. A new paradigm for computation, the super-recursive algorithm, offers promising prospects for algorithms of much greater computing power and efficiency. Super-Recursive Algorithms provides an accessible, focused examination of the theory of super-recursive algorithms and its ramifications for the computer industry, networks, artificial intelligence, embedded systems, and the Internet. The book demonstrates how these algorithms are more appropriate as mathematical models for modern computers, and how these algorithms present a better framework for computing methods in such areas as numerical analysis, array searching, and controlling and monitoring systems. In addition, a new practically-oriented perspective on the theory of algorithms, computation, and automata, as a whole, is developed. Problems of efficiency, software development, parallel and distributed processing, pervasive and emerging computation, computer architecture, machine learning, brain modeling, knowledge discovery, and intelligent systems are addressed.
Statistical Implicative Analysis : Theory and Applications
This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.
Spatial Cognition VI. Learning, Reasoning, and Talking about Space ; International Conference Spatial Cognition 2008, Freiburg, Germany, September 15-19, 2008. Proceedings
This book includes spatial orientation, spatial navigation, spatial learning, maps and modalities, spatial communication, spatial language, similarity and abstraction, concepts and reference frames, as well as spatial modeling and spatial reasoning.
Software and Data Technologies ; 1st International Conference, ICSOFT 2006, Setúbal, Portugal, September 11-14, 2006, Revised Selected Papers
This book is organized in topical sections on programming languages, software engineering, distributed and parallel systems, information systems and data management, as well as knowledge engineering.
Soft Computing for Knowledge Discovery and Data Mining
The first three parts of this book are devoted to the principal constituents of soft computing: neural networks, evolutionary algorithms and fuzzy logic. The last part compiles the recent advances in soft computing for data mining, such as swarm intelligence, diffusion process and agent technology. This book provides investigators in the fields of information systems, engineering, computer science, operations research, bio-informatics, statistics and management with a profound source for the role of soft computing in data mining.
SOFSEM 2008: Theory and Practice of Computer Science ; 34th Conference on Current Trends in Theory and Practice of Computer Science, Nový Smokovec, Slovakia, January 19-25, 2008. Proceedings
This book is segmented into four topical sections on foundations of computer science; computing by nature; networks, security, and cryptography; and Web technologies.
Social Computing, Behavioral Modeling, and Prediction
Social computing concerns the study of social behavior and context based on computational systems. Behavioral modeling reproduces the social behavior, and allows for experimenting, scenario planning, and deep understanding of behavior, patterns, and potential outcomes. The pervasive use of computer and Internet technologies creates an unprecedented environment where people can share opinions and experiences, exchange ideas, offer suggestions and advice, debate and even conduct experiments. Social computing facilitates behavioral modeling in model building, analysis, pattern mining, anticipation, and prediction. This volume presents material from the first interdisciplinary workshop focused on employing social computing for behavioral modeling and prediction. The book provides a platform for disseminating results and developing new concepts and methodologies aimed at advancing and deepening our understanding of social and behavioral computing to aid critical decision making. The contributions incorporate views from government, industry and academia and they address research problems arising from pressing demands in the real world.
Snowflake Essentials : Getting Started with Big Data in the Cloud
Understand the essentials of the Snowflake Database and the overall Snowflake Data Cloud. This book covers how Snowflake’s architecture is different from prior on-premises and cloud databases. The authors also discuss, from an insider perspective, how Snowflake grew so fast to become the largest software IPO of all time. You will learn : Run analytics in the Snowflake Data Cloud / Create users and roles in Snowflake / Set up security in Snowflake / Set up resource monitors in Snowflake / Set up and optimize Snowflake Compute / Load, unload, and query structured and unstructured data (JSON, XML) within Snowflake / Use Snowflake Data Sharing to share data / Set up a Snowflake Data Exchange / Use the Snowflake Data Marketplace
Smart Sensing and Context ; 3rd European Conference, EuroSSC 2008, Zurich, Switzerland, October 29-31, 2008. Proceedings
This book is organized in topical sections on smart objects, spatial and human context inference, context processing and quality of context, as well as context-aware interaction and case studies.
Smart Homes and Health Telematics ; 6th International Conference, ICOST 2008 Ames, IA, USA, June 28-July 2, 2008 Proceedings
The book is organized in topical sections on assistive technology to improve quality of life for older adults and their caregivers; context awareness / autonomous computing; devices, systems and algorithms for vision / hearing / cognitive / communication impairments; home health monitoring and intervention; human-machine interface and ambient intelligence; modeling of physical and conceptual information in intelligent environments; real world deployments and experiences in smart homes, hospitals, and living communities; and social/privacy/security issues.
Service-Oriented Computing ; Agents, Semantics, and Engineering ; AAMAS 2008 International Workshop, SOCASE 2008, Estoril, Portugal, May 12, 2008 Proceedings
The book address a range of topics at the intersection of service-oriented computing, semantic technology, and intelligent multiagent systems, such as: service description and discovery; planning, composition and negotiation; semantic processes and service agents; and applications.
Semantics, Web and Mining ; Joint International Workshop, EWMF 2005 and KDO 2005, Porto, Portugal, October 3-7, 2005, Revised Selected Papers
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge differ among knowledge discovery (KD) projects and approaches. One central question is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central question is whether the background knowledge, business understanding, etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between.
Semantics in Data and Knowledge Bases ; 3rd International Workshop, SDKB 2008, Nantes, France, March 29, 2008, Revised Selected Papers
This book presented original contributions demonstrating the use of logic, discrete mathematics, combinatorics, domain theory and other mathematical theories of semantics for database and knowledge bases, computational linguistics and semiotics, and information and knowledge-based systems.
