As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more; algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods; performance improvement techniques that work by transforming the input or output; and, downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface.

The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.

Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g., in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the stateof- the-art in certain areas. This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

《神经网络与机器学习(英文版第3版)》的可读性非常强，作者举重若轻地对神经网络的基本模型和主要学习理论进行了深入探讨和分析，通过大量的试验报告、例题和习题来帮助读者更好地学习神经网络。神经网络是计算智能和机器学习的重要分支，在诸多领域都取得了很大的成功。在众多神经网络著作中，影响最为广泛的是SimonHaykin的《神经网络原理》(第4版更名为《神经网络与机器学习》）。在《神经网络与机器学习(英文版第3版)》中，作者结合近年来神经网络和机器学习的最新进展，从理论和实际应用出发，全面。系统地介绍了神经网络的基本模型、方法和技术，并将神经网络和机器学习有机地结合在一起。《神经网络与机器学习(英文版第3版)》不但注重对数学分析方法和理论的探讨，而且也非常关注神经网络在模式识别、信号处理以及控制系统等实际工程问题中的应用。 本版在前一版的基础上进行了广泛修订，提供了神经网络和机器学习这两个越来越重要的学科的最新分析。

《计算智能:从概念到实现(英文版)》面向智能系统学科的前沿领域，系统地讨论了计算智能的理论、技术及其应用，比较全面地反映了计算智能研究和应用的最新进展。书中涵盖了模糊控制、神经网络控制、进化计算以及其他一些技术及应用的内容。《计算智能:从概念到实现(英文版)》提供了大量的实用案例，重点强调实际的应用和计算工具，这些对于计算智能领域的进一步发展是非常有意义的。《计算智能:从概念到实现(英文版)》取材新颖，内容深入浅出，材料丰富，理论密切结合实际，具有较高的学术水平和参考价值。 《计算智能:从概念到实现(英文版)》可作为高等院校相关专业高年级本科生或研究生的教材及参考用书，也可供从事智能科学、自动控制、系统科学、计算机科学、应用数学等领域研究的教师和科研人员参考。

本书是最全面的机器学习教材之一。书中首先介绍了机器学习的构成要素（任务、模型、特征）和机器学习任务，接着详细分析了逻辑模型（树模型、规则模型）、几何模型（线性模型和基于距离的模型）和概率模型，然后讨论了特征、模型的集成，以及被机器学习研究者称为“实验”的方法。作者不仅使用了已有术语，还引入了一些新的概念，同时提供了大量精选的示例和插图解说。

《自然计算：DNA、量子比特和智能机器的未来》介绍了16位致力于解决计算领域前沿问题的科学家，他们分别在科学、工程金融等领域从事极富挑战性的工作。《自然计算：DNA、量子比特和智能机器的未来》记录了与这些科学家的对话内容，描绘了新的计算机架构和丰富多彩的新型软件技术。书中每章自成一体，揭示了这些科学家独特的探索之路；同时，还介绍了作者写作过程中产生的一系列奇思妙想，这些思想注定会让这个世界变得更好。 《自然计算：DNA、量子比特和智能机器的未来》适合所有对未来智能机器及未来计算感兴趣的读者阅读。

This book presents cutting-edge game AI techniques primarily from game developers of shipped commercial games as well as some from academia. Topics covered include AI architectures, steering and path finding, tactics/strategy, genre specific techniques, gesture recognition, and learning techniques. About 60 articles are presented in a style that is technical yet accessible to beginners, but ultimately aimed at the intermediate to expert professional game developer. The book supplies readers with a toolbox of techniques that are applicable to a wide range of situations.

神经网络是计算智能和机器学习的重要分支，在诸多领域都取得了很大的成功。在众多神经网络著作中，影响最为广泛的是Simon Haykin的《神经网络原理》（第3版更名为《神经网络与机器学习》）。在本书中，作者结合近年来神经网络和机器学习的最新进展，从理论和实际应用出发，全面、系统地介绍了神经网络的基本模型、方法和技术，并将神经网络和机器学习有机地结合在一起。 本书不但注重对数学分析方法和理论的探讨，而且也非常关注神经网络在模式识别、信号处理以及控制系统等实际工程问题的应用。本书的可读性非常强，作者举重若轻地对神经网络的基本模型和主要学习理论进行了深入探讨和分析，通过大量的试验报告、例题和习题来帮助读者更好地学习神经网络。 本版在前一版的基础上进行了广泛修订，提供了神经网络和机器学习这两个越来越重要的学科的最新分析。 本书特色： 1. 基于随机梯度下降的在线学习算法；小规模和大规模学习问题。 2. 核方法，包括支持向量机和表达定理。 3. 信息论学习模型，包括连接、独立分量分析（ICA）、一致独立分量分析和信息瓶颈。 4. 随机动态规划，包括逼近和神经动态规划。 5. 逐次状态估计算法，包括卡尔曼和粒子滤波器。 6. 利用逐次状态估计算法训练递归神经网络。 7. 富有洞察力的面向计算机的试验。

In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging - and beautiful - working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With "Beautiful Data", you will: explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web; learn how to visualize trends in urban crime, using maps and data mashups; discover the challenges of designing a data processing system that works within the constraints of space travel; also learn how crowdsourcing and transparency have combined to advance the state of drug research; and, understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data. Learn about the massive infrastructure required to create, capture, and process DNA data. That's only small sample of what you'll find in "Beautiful Data". For anyone who handles data, this is a truly fascinating book. Contributors include: Nathan Yau; Jonathan Follett and Matt Holm; J.M. Hughes; Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava; Jeff Hammerbacher; Jason Dykes and Jo Wood; Jeff Jonas and Lisa Sokol; Jud Valeski; Alon Halevy and Jayant Madhavan; Aaron Koblin and Valdean Klump; Michal Migurski; Jeff Heer; Coco Krumme; Peter Norvig; Matt Wood and Ben Blackburne; Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen; Lukas Biewald and Brendan O'Connor; Hadley Wickham, Deborah Swayne, and David Poole; Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza; and, Toby Segaran.

《数据挖掘实用机器学习技术(原书第2版)》介绍数据挖掘的基本理论与实践方法。主要内容包括：各种模型(决策树、关联规则、线性模型、聚类、贝叶斯网以及神经网络)以及在实践中的运用，所存在缺陷的分析。安全地清理数据集、建立以及评估模型的预测质量的方法，并且提供了一个公开的数据挖掘工作平台Weka。Weka系统拥有进行数据挖掘任务的图形用户界面，有助于理解模型，是一个实用并且深受欢迎的工具。 海报：

《不确定性人工智能》讨论了人类知识和智能中不确定性存在的客观性、普遍性和积极意义,围绕不确定性人工智能的数学基础、特征、表示、模型、推理机制、不确定性思维活动中的确定性等进行研究,从定性定量转换模型——云模型,认知的物理学方法——数据场、云变换、发现状态空间理论,到数据挖掘、知识发现和智能控制逐层展开,寻找不确定性知识和智能处理中的规律性,最后对不确定性人工智能研究的发展方向进行了展望。

《统计自然语言处理基础：国外计算机科学教材系列》是一本全面系统地介绍统计自然语言处理技术的专著，被国内外许多所著名大学选为计算语言学相关课程的教材。《统计自然语言处理基础：国外计算机科学教材系列》涵盖的内容十分广泛，分为四个部分，共16章，包括了构建自然语言处理软件工具将用到的几乎所有理论和算法。全书的论述过程由浅入深，从数学基础到精确的理论算法，从简单的词法分析到复杂的语法分析，适合不同水平的读者群的需求。同时，《统计自然语言处理基础：国外计算机科学教材系列》将理论与实践紧密联系在一起，在介绍理论知识的基础上给出了自然语言处理技术的高层应用（如信息检索等）。在《统计自然语言处理基础：国外计算机科学教材系列》的配套网站上提供了许多相关资源和工具，便于读者结合书中习题，在实践中获得提高。近年来，自然语言处理中的统计学方法已经逐渐成为主流。

本书详细介绍基于核的模式分析的基本概念及其应用，主要内容包括：主要理论基础，若干基于核的算法，从最简单的到较复杂的系统，例如核偏序最小二乘法、典型相关分析、支持向量机、主成分分析等。还描述了若干核函数，从基本的例子到高等递归核函数，从生成模型导出的核函数(如HMM)到基于动态规划的串匹配核函数，以及用于处理文本文档的特殊核函数等。 本书适用于所有从事模式识别、机器学习、神经网络及其应甩的学生、教师和研究人员。

《神经计算原理》比较系统全面地介绍了人工神经网络的理论和实际应用，特别在神经网络模型和工程应用方面有极为深入的分析和讲解。全书不仅深入分析神经网络的基本概念，而且详细介绍神经网络应用方面的最新发展趋势和主要研究方向。《神经计算原理》理论和实际应用紧密结合，为神经网络的相关理论知识在具体问题中的应用打下了坚实的基础。

《人工神经网络导论》依照简明易懂、便于软件实现、鼓励探索的原则介绍人工神经网络。内容包括：智能系统描述模型，人工神经网络方法的特点；基本人工神经元模型，人工神经网络的基本拓扑特性，存储性能及学习；感知器与线性不可分问题，Ifcc学习律，Efmub规则；CQ算法及其原理分析，算法改进讨论；对传网的结构及其运行，对传网的初始化与训练算法；统计网络的训练与收敛性分析；Ipgjqfme 网络及稳定性，Boltzmann 机；双联存储网络的结构及训练；BSU 模型的结构分析与实现。 《人工神经网络导论》适合于研究生和本科高年级学生使用，也可供有关学生、科技人员参考。

《蚁群算法原理及其应用(精装)》系统、深入地介绍了蚁群算法的原理及其应用，力图概括国内外在这一学术领域的最新研究进展。全书共包括10章，主要内容包括蚁群算法的思想起源、研究现状及机制原理；蚁群算法的复杂度分析；蚁群算法的收敛性证明；蚁群算法参数对其性能的影响；蚁群算法的参数选择原则；离散域和连续域蚁群算法的若干改进策略；蚁群算法在多个优化领域的典型应用；蚁群算法的硬件实现技术；蚁群算法与其他仿生优化算法的比较与融合；蚁群算法的研究展望；最后还在附录部分给出了基本蚁群算法的程序源代码和相关网站。

内容简介： 随着信息技术和互联网的发展，人们逐渐从信息匮乏的时代走入了信息过载（information overload）的时代 。在这个时代，无论是信息消费者还是信息生产者都遇到了很大的挑战：对于信息消费者，从大量信息中找到自己感兴趣的信息是一件非常困难的事情；对于信息生产者，让自己生产的信息脱颖而出，受到广大用户的关注，也是一件非常困难的事情。推荐系统就是解决这一矛盾的重要工具。推荐系统的任务就是联系用户和信息，一方面帮助用户发现对自己有价值的信息，另一方面让信息能够展现在对它感兴趣的用户面前，从而实现信息消费者和信息生产者的双赢。

It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades. "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.