这个程序是关于现有的3G系统中,采用TDOA和pattern matching的方法实现定位的仿真程序.(the procedure is available on the 3G system, using TDOA and pattern matching method of positioning the simulation program.)

1) golang 的设计模式 GO is a new programming languages developed at Google by Robert Griesemer, Rob Pike, Ken Thompson, and others. GO was published in November 2009 and made open source; was “Language of the year” 2009 [7]; and was awarded the Bossie Award 2010 for “best open source application development software” [1]. GO deserves an evaluation. Design patterns are records of idiomatic programming practice and inform programmers about good program design. Design patterns provide generic solutions for reoccurring problems and have been implemented in many programming languages. Every programming language has to solve the problems addressed by patterns. In this thesis we use design patterns to evaluate the innovative features of GO.

svr算法matlab代码Pattern_Regression 这是我们NeuroImage论文（）的代码，其中涉及不同模式回归算法（即OLS，Ridge，LASSO，Elastic-Net，SVR，RVR）的比较以及样本量对预测性能的影响。 如果您想在工作中进行个性化的行为预测。 最好尝试使用中的代码。 这里的代码更特定于此研究。 如果您使用这些代码，将不胜感激引用我们的相关论文。 Zaixu Cui, Gaolang Gong, The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features, (2018), NeuroImage, 178: 622-37 Zaixu Cui, et al., Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volum

滤色器阵列 (CFA) 去马赛克使用模式识别插值技术执行图像去马赛克，如参考论文的第 2.6 节所述。 该技术在执行去马赛克时保留了边缘。

经典的机器学习的教材 高清 英文版 作者Christopher M. Bishop

机器学习的经典基础教程，扫描清晰版，值得一看！

Bishop - Pattern Recognition And Machine Learning - Springer 2006

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

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.　　

非常好的设计模式资料，对designed patterns中涉及的设计模式进行解释，是面向对象设计人员的必备资料