这本书是Ovidiu Furdui在过去十年中教授，研究和解决问题的成果。 本书提供了一个不寻常的问题集合，专门研究数学分析的三个主题：极限，级数和分数部分积分。 全书共分三章，每章分别讨论一个具体的题目和两个附录。 每一章都包含一些由书中的其他问题所激发的一些难题，这些难题被收集在一个题为“未解决的问题”的特别小节中，其中很少以问题出现在书中的顺序列出。 这些问题可以考虑作为研究问题或项目给有微积分背景的学生，以及喜欢数学研究和数学发现的读者。

Standard Springer book templates are available for both LaTeX format.

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems. This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

We are delighted to introduce the proceedings of the second edition of the 2017 European Alliance for Innovation (EAI) International Conference on Machine Learning and Intelligent Communications (MLICOM). This conference brought together researchers, developers, and practitioners from around the world who are leveraging and developing machine learning and intelligent communications. The technical program of MLICOM 2017 consisted of 141 full papers in oral presentation sessions at the main conference tracks. The conference tracks were: Main Track, Machine Learning; Track 1, Intelligent Positioning and Navigation; Track 2, Intelligent Multimedia Processing and Security; Track 3, Intelligent Wireless Mobile Network and Security; Track 4, Cognitive Radio and Intelligent Networking; Track 5, Intelligent Internet of Things; Track 6, Intelligent Satellite Communications and Networking; Track 7, Intelligent Remote Sensing, Visual Computing and Three-Dimensional Modeling; Track 8, Green Communication and Intelligent Networking; Track 9, Intelligent Ad-Hoc and Sensor Networks; Track 10, Intelligent Resource Allocation in Wireless and Cloud Networks; Track 11, Intelligent Signal Processing in Wireless and Optical Communications; Track 12, Intelligent Radar Signal Processing; Track 13, Intelligent Cooperative Communications and Networking. Aside from the high-quality technical paper presentations, the technical program also featured three keynote speeches. The three keynote speeches were by Prof. Haijun Zhang from the University of Science and Technology Beijing, China, Prof. Yong Wang from Harbin Institute of Technology, China, and Mr. Lifan Liu from National Instruments China. Coordination with the steering chairs, Imrich Chlamtac, Xuemai Gu, and Gongliang Liu, was essential for the success of the conference. We sincerely appreciate their constant support and guidance. It was also a great pleasure to work with such an excellent Organizing Committee who worked hard to organize

找个Springer的LNCS格式真难，下载了好多都没用，还浪费了好多积分。最后终于从老师那里要到了，分享一下，是doc的，

经典英文原版电动力学教材 Springer图书，内容全面，讲解详细，经典教材，并且能够提升英语能力

这是Springer的官方Latex模板，如有需要请大家尽情下载。

今天在弄Lecture Note in Computer Science的format，在EndNote中卻找不到LNCS的style，然后在LNCS的website上也找不到。 终于从别人那儿要到了一个正宗的LNCS的style文件 只要在Style or Journal Name輸入 "Lecture"，然後點擊"Apply"。

Time Series Analysis With Applications in R (Springer)

高清PDF电子书， 关于卡尔曼滤波和小波的，经典书籍，第四版了 Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and minimum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fire control. With the recent development of high-speed computers, the Kalman filter has become more useful even for very complicated real-time applications. In spite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most practitioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thorough discussion of its mathe- matical theory and applications to various elementary real-time problems