A systematic treatment of Boolean reasoning, this concise, newly revised edition combines the works of early logicians with recent investigations, including previously unpublished research results. The book begins with an overview of elementary mathematical concepts and outlines the theory of Boolean algebras. Two concluding chapters deal with applications. 1990 edition. Includes 18 figures and 19 tables.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Computational geometry emerged from the field of algorithms design and analysis in the late 1970s. It has grown into a recognized discipline with its own joumals， conferences， and a large community of active researchers. The success of the field as a research discipline can on the one hand be explained from the beauty of the problems studied and the solutions obtained， and， on the other hand， by the many application domains-computer graphics， geographic information systems （GIS）， robotics， and others-in which geometric algonthms play a fundamental role.
For many geometric problems the early algorithmic solutions were either slow or difficult to understand and implement. In recent years a number of new algorithmic techruques have been developed that improved and simplified many of the previous approaches. In this textbook we have tried to make these modem algorithmic solutions accessible to a large audience. The book has been written as a textbook for a course in computational geometry， but it can also be used for self-study.

本书全面介绍了数据挖掘的理论和方法，着重介绍如何用数据挖掘知识解决各种实际问题，涉及学科领域众多，适用面广。
书中涵盖5个主题：数据、分类、关联分析、聚类和异常检测。除异常检测外，每个主题都包含两章：前面一章讲述基本概念、代表性算法和评估技术，后面一章较深入地讨论高级概念和算法。目的是使读者在透彻地理解数据挖掘基础的同时，还能了解更多重要的高级主题。
本书特色
·包含大量的图表、综合示例和丰富的习题。
·不需要数据库背景，只需要很少的统计学或数学背景知识。
·网上配套教辅资源丰富，包括ppt、习题解答、数据集等。