《牛津计算语言学手册》内容简介：《牛津计算语言学手册》是一部手册性的计算语言学专著，收录了包括语言学家、计算机专家和语言工程人员在内的50位学者撰写的综述性文章，全面地反映了国外计算语言学主要领域的最新成果，是我们了解国外计算语言学发展动向的一个窗口。 全书各章写作风格一致，内容协调，浑然一体，使用有趣的实例来介绍艰深的技术问题，而且尽量不使用繁难的数学公式，尤其适合文科背景的读者阅读。对于那些对计算语言学感兴趣和刚入门的读者而言，《牛津计算语言学手册》也是一本必备的参考书。

本书是数理逻辑方面的经典教材。书中涵盖了命题逻辑、一阶逻辑、不可判定性以及二阶逻辑等方面的内容，并且包含本书是数理逻辑方面的经典教材。书中涵盖了命题逻辑、一阶逻辑、不可判定性以及二阶逻辑等方面的内容，并且包含了与计算机科学有关的主题，如有限模型。本书特点是：内容可读性强；组织结构更灵活，授课教师可根据教学需要节选本书的内容；反映了近几年来理论计算机科学对逻辑学产生的影响；包含较多的示例和说明。本书适合作为计算机及相关专业本科生和研究生数理逻辑课程的教材。.
本书是数理逻辑方面的经典教材，以可读性强而著称，在美国大学中采用率极高，麻省理工学院、加州大学伯克利分校、哥伦比亚大学、康奈尔大学等众多名校均用它作为教材。本版章节组织更加灵活，增加了与计算机科学相关的主题(比如有限模型)，还增加了一些示例和阐释文字，更适合本科生和研究生数理逻辑课程使用。.

本书由地理信息系统(GIS)领域的四位资深学者编写，全面系统地讲解地理信息系统的基本原理与应用，是该领域的优秀教材。本书的主要内容包括：GIS应用、地理表达、地理数据的特性、GIS软件、利用GIS进行空间建模、分布式GIS、地理可视化、管理GIS等。本书实例丰富、讲解透彻、提供大量实际图片和教学资源，可作为地理信息课程的教材或参考书，也可供GIS专业人员参考。.
本书由GIS领域的四位资源学者编写,广受学生和GIS 从业人员的喜爱,是GIS方面重要的教材和参考书。它的独到之处在于，以一种浅显易懂的方法将GIS的多样性和丰富性联系在一起，将GIS相关基础理论问题讲得十分透彻。第2版再加强调将GIS作为通往科学和问题解决之路的观点，提出了指导GIS应用的科学原理，并描述了对GIS的发展，设计和成功产生影响的人类活动。第2版涵盖了GIS新的重要研究领域。
●基于位置的服务。..
●分布式计算。
●虚拟和增强现实。
●国土安全。
●GIS业务和地理人口统计。
●地理入口的出现。
●地理信息面临的巨大挑战。
本书为教师和学生提供在线资http://www.wiley.com/go/longley。
本书可以作为地理、环境、商务（或公关）管理、计算机科学、城市研究、规划、信息科学、土木工程、考古等众多专业本科生或研究生的教材或参考书。GIS专业技术人员也可以从本书大量的实用的资源中获益。

This second edition of Grune and Jacobs' brilliant work presents new developments and discoveries that have been made in the field. Parsing, also referred to as syntax analysis, has been and continues to be an essential part of computer science and linguistics. Parsing techniques have grown considerably in importance, both in computer science, ie. advanced compilers often use general CF parsers, and computational linguistics where such parsers are the only option. They are used in a variety of software products including Web browsers, interpreters in computer devices, and data compression programs; and they are used extensively in linguistics.

数千名微软精锐开发人员的经验和智慧，最终浓缩在这本设计规范之中。与上一版相比，书中新增了许多评注，解释了相应规范的背景和历史，从中你能聆听到微软技术大师Anders Hejlsberg、Jeffrey Richter和Paul Vick等的声音，读来令人兴味盎然。
本书虽然是针对.NET平台上的框架设计的，但对其他平台的框架设计同样具有借鉴意义。新版根据.NET Framework 3.0和3.5的新特性做了全面更新，主要关注的是直接影响框架可编程能力的设计问题。遵守这些规范对于使用.NET Framework创建高质量的应用程序至关重要。
本书提供配套光盘，内含Designing .NET Class Libraries等13个演讲视频。此外，光盘还包括.NET Framework类和组件设计指南、API规范样例以及其他有用的资源和工具。

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

《数据库索引设计与优化》提供了一种简单、高效、通用的关系型数据库索引设计方法。作者通过系统的讲解及大量的案例清晰地阐释了关系型数据库的访问路径选择原理，以及表和索引的扫描方式，详尽地讲解了如何快速地估算SQL 运行的CPU 时间及执行时间，帮助读者从原理上理解SQL、表及索引结构、访问方式等对关系型数据库造成的影响，并能够运用量化的方法进行判断和优化，指导关系型数据库的索引设计。
《数据库索引设计与优化》适用于已经具备了SQL 这一关系型语言相关知识，希望通过理解SQL 性能相关的内容，或者希望通过了解如何有效地设计表和索引而从中获益的人员。另外，《数据库索引设计与优化》也同样适用于希望对新硬件的引入所可能带来的变化做出更好判断的资深人士。

Windows环境下32位汇编语言是一种全新的编程语言。它使用与C++语言相同的API接口，不仅可以开发出大型的软件，而且是了解操作系统运行细节的最佳方式。
本书从编写应用程序的角度，从“Hello，World!”这个简单的例子开始到编写多线程、注册表和网络通信等复杂的程序，通过70多个实例逐步深入Win32汇编语言编程的方方面面。
本书作者罗云彬拥有十余年汇编语言编程经验，是汇编编程网站http://www.win32asm.com.cn的创办者。本书是作者多年来编程工作的总结，适合于欲通过Win32汇编语言编写Windows程序的读者。

本书从实践角度介绍安全漏洞，描述了作者在过去几年里怎样发现漏洞、怎样利用漏洞来攻击以及开发商如何修复漏洞，旨在为开发人员提醒，为漏洞研究领域的工作人员提供工作思路。

《UNIX网络编程》(第1卷)(套接口API第3版)第1版和第2版由已故UNIX网络专家W. Richard Stevens博士独自编写。《UNIX网络编程》(第1卷)(套接口API第3版)是3版，由世界著名网络专家Bill Fenner和Andrew M. Rudoff执笔，根据近几年网络技术的发展，对上一版进行全面修订，增添了IPv6的更新过的信息、SCTP协议和密钥管理套接口的内容，删除了X/Open传输接口的内容。
《UNIX网络编程》(第1卷)(套接口API第3版)内容详尽且具权威性，几乎每章都提供精选的习题，是计算机和网络专业高年级本科生和研究生的首选教材，《UNIX网络编程》(第1卷)(套接口API第3版)也可作为网络研究和开发人员的自学教材和参考书。

本书是被誉为UNIX编程“圣经”的Advanced Programming in the UNIX Environment一书的更新版。在本书第1版出版后的十几年中，UNIX行业已经有了巨大的变化，特别是影响UNIX编程接口的有关标准变化很大。本书在保持了前一版风格的基础上，根据最新的标准对内容进行了修订和增补，反映了最新的技术发展。书中除了介绍UNIX文件和目录、标准I/O库、系统数据文件和信息、进程环境、进程控制、进程关系、信号、线程、线程控制、守护进程、各种I/O、进程间通信、网络IPC、伪终端等方面的内容，还在此基础上介绍了多个应用示例，包括如何创建数据库函数库以及如何与网络打印机通信等。此外，还在附录中给出了函数原型和部分习题的答案。
本书内容权威，概念清晰，阐述精辟，对于所有层次UNIX程序员都是一本不可或缺的参考书。

本书主要介绍了Unix系统领域中的设计和开发哲学、思想文化体系、原则与经验，由公认的Unix编程大师、开源运动领袖人物之一Eric S. Raymond倾力多年写作而成。包括Unix设计者在内的多位领域专家也为本书贡献了宝贵的内容。本书内容涉及社群文化、软件开发设计与实现，覆盖面广、内容深邃，完全展现了作者极其深厚的经验积累和领域智慧。

《人工智能:一种现代方法》(第2版中文版)以详尽和丰富的资料，从理性智能体的角度，全面阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向，是一本难得的综合性教材。全书分为八大部分：第一部分“人工智能” ，第二部分“问题求解” ，第三部分“ 知识与推理” ，第四部分“规划” ，第五部分“不确定知识与推理” ，第六部分“学习” ，第七部分“通讯、感知与行动” ，第八部分“ 结论” 。

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Class-tested and coherent, this groundbreaking new textbook teaches classic web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.
Contents
1. Information retrieval using the Boolean model; 2. The dictionary and postings lists; 3. Tolerant retrieval; 4. Index construction; 5. Index compression; 6. Scoring and term weighting; 7. Vector space retrieval; 8. Evaluation in information retrieval; 9. Relevance feedback and query expansion; 10. XML retrieval; 11. Probabilistic information retrieval; 12. Language models for information retrieval; 13. Text classification and Naive Bayes; 14. Vector space classification; 15. Support vector machines and kernel functions; 16. Flat clustering; 17. Hierarchical clustering; 18. Dimensionality reduction and latent semantic indexing; 19. Web search basics; 20. Web crawling and indexes; 21. Link analysis.
Reviews
“This is the first book that gives you a complete picture of the complications that arise in building a modern web-scale search engine. You'll learn about ranking SVMs, XML, DNS, and LSI. You'll discover the seedy underworld of spam, cloaking, and doorway pages. You'll see how MapReduce and other approaches to parallelism allow us to go beyond megabytes and to efficiently manage petabytes." -Peter Norvig, Director of Research, Google Inc.
"Introduction to Information Retrieval is a comprehensive, up-to-date, and well-written introduction to an increasingly important and rapidly growing area of computer science. Finally, there is a high-quality textbook for an area that was desperately in need of one." -Raymond J. Mooney, Professor of Computer Sciences, University of Texas at Austin
“Through compelling exposition and choice of topics, the authors vividly convey both the fundamental ideas and the rapidly expanding reach of information retrieval as a field.” -Jon Kleinberg, Professor of Computer Science, Cornell University

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. -Sanjay Ranka, University of Florida In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules). -Mohammed Zaki, Rensselaer Polytechnic Institute

《群智能:介绍与应用》是一部关于群智能方法及其应用的专业书籍。第一部分导论介绍群智能、优化和群机器人的生物学基础，以及在新一代通信网络中的应用。优化和群机器人学是现今群智能原理应用非常成功的两个领域。第二部分的章节包含群智能研究的一些具体实例，例如，机器人行为的演变、粒子群动态优化、有机计算和生产网络中的非集中式流量。书中的八章相对独立，读者可以根据自己的研究领域和兴趣选择其中相应的章节阅读。《群智能:介绍与应用》适合以人工智能、通信网络或生物信息学为研究方向的理工科大学研究生和教师阅读，也可供相关研究领域的科研人员参考。

本书是国际著名声学家安藤四一敎授继1985年出版《音乐厅声学》之后的又一力作。本书论证了声源信号如何与厅堂声场相融合，探求声场优选参量与声源信号以及听觉和大脑听觉信息处理机制之间的关系。本书介绍了作者将其理论应用于日本雾岛音乐厅的音质设计的经验，以及在该厅率先实现的座位音质选择系统。本书对于建筑厅堂音质设计、计算机仿真声场和可听化技术均具有深入的阐述。本书由中国科学院院士吴硕贤教授和赵越喆副教授翻译，是这一領域不可多得的一本前沿性著作。

《模式分类》(原书第2版)的第1版《模式分类与场景分析》出版于1973年，是模式识别和场景分析领域奠基性的经曲名著。在第2版中，除了保留了第1版的关于统计模式识别和结构模式识别的主要内容以外，读者将会发现新增了许多近25年来的新理论和新方法，其中包括神经网络、机器学习、数据挖掘、进化计算、不变量理论、隐马尔可夫模型、统计学习理论和支持向量机等。作者还为未来25年的模式识别的发展指明了方向。书中包含许多实例，各种不同方法的对比，丰富的图表，以及大量的课后习题和计算机练习。