好用的位图转矢量图软件：Vector Magic

google 搞的word2vector，将词转向量工具。可以借这个工具阅读代码

Vector Magic 1.15汉化绿色特别版使用说明： 1：在本文地址最后下载Vector Magic 1.15汉化绿色特别版，解压出来。 2：找到压缩包中的Vector Magic注册工具，并运行，点击一下第一个按钮即可（点击一下即可完成注册，注册工具会自动注册文件）。 注：由于程序加壳的原因，所以原版程序、汉化文件、注册工具都有可能被报毒，害怕者请勿使用（原版加壳应该是为了防止被注册，汉化文件加壳是为了防止被修改汉化内容，注册工具报毒那都是众所周知的原因了） 个人建议，可以先把Vector Magic中文版的操作弄熟悉，再去用Vector Magic英文版。 Vector Magic的简单操作教程如下: 1、打开你需要转换的图片。 2、完成2到3个步骤的设置向导，其实就是回答几个简单的选项，以帮助您更好的完成转换。 3、保存转换后的矢量效果图片。可以在软件界面查看转换效果，如果您比较懂的话还可以编辑细节。

Vector Magic Desktop 汉化绿色版 位图转矢量图 附带注册机

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.