A_Tutorial_on_Support_Vector_Machines_for_Pattern_Recognition这篇文章的翻译，自己辛苦翻译的

机器人轨迹生成经典书籍 This book deals with the problems related to planning motion laws and trajectories for the actuation system of automatic machines, in particular for those based on electric drives, and robots. The problem of planning suitable trajectories is relevant not only for the proper use of these machines, in order to avoid undesired effects such as vibrations or even damages on the mechanical structure, but also in some phases of their design and in the choice and sizing of the actuators. This is particularly true now that the concept of “electronic cams” has replaced, in the design of automatic machines, the classical approach based on “mechanical cams”. The choice of a particular trajectory has direct and relevant implications on several aspects of the design and use of an automatic machine, like the dimensioning of the actuators and of the reduction gears, the vibrations and efforts generated on the machine and on the load, the tracking errors during the motion execution. For these reasons, in order to understand and appreciate the peculiarities of the different techniques available for trajectory planning, besides the mathematical aspects of their implementation also a detailed analysis in the time and frequency domains, a comparison of their main properties under different points of view, and general considerations related to their practical use are reported.

RBM在深度学习（deep learning）算法中有着非常重要的应用，本文介绍了RBM的基本概念，并介绍了几种有代表性的算法。作者西安交大张春霞，姬楠楠，王冠伟。

Learning with Kernels - Support Vector Machines

A tutorial on support vector machines for pattern recognition 的原文和对应的中文翻译 是SVM最为经典的资料之一

Deep Learning using Linear Support Vector Machines的简单实现代码

这是做电机矢量控制必有的一本教材，好多的著名的书籍都引用了这本书，大家可以看看原版是什么样的，很清晰

pdf类型的轨迹规划外文权威书籍，自己花钱买的。

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.