《现代模式识别》系统深入地论述了模式识别的理论与方法、较全面地介绍了本学科的新近科技成果。全书共12章，讨论的主流模式识别技术是：统计模式识别、模糊模式识别、神经网络技术、人工智能方法、句法模式识别。第一章为引论，第二章至第七章介绍的统计模式识别包括聚类分析、判别代数界面方程法、统计判决、训练学习与错误率估计、特征提取与选择以及最近邻法，第十一章信息融合集中论述识别与决策中的有关融合技术，第十二章人工智能方法侧重论述不确定推理，其他类型识别方法在其余各章分别介绍。

《统计学习基础:数据挖掘、推理与预测》介绍了这些领域的一些重要概念。尽管应用的是统计学方法，但强调的是概念，而不是数学。许多例子附以彩图。《统计学习基础:数据挖掘、推理与预测》内容广泛，从有指导的学习（预测）到无指导的学习，应有尽有。包括神经网络、支持向量机、分类树和提升等主题，是同类书籍中介绍得最全面的。计算和信息技术的飞速发展带来了医学、生物学、财经和营销等诸多领域的海量数据。理解这些数据是一种挑战，这导致了统计学领域新工具的发展，并延伸到诸如数据挖掘、机器学习和生物信息学等新领域。许多工具都具有共同的基础，但常常用不同的术语来表达。

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.

《模式分类》(英文版)(第2版)简明易读，新增的图表使得许多统计和数学题材非常生动。最终以完美和谐的形式，引导读者深入新的主题。

This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.