转载：c++开源机器学习库（更新中20140330）
1）mlpack is a C++ machine learning library.[按：据说代码风格比shogun强多了;另外可以在win上编译的指南是：https://trac.research.cc.gatech.edu/fastlab/wiki/MLPACKOnWindows]
2）PLearn is a C++ library aimed at research and development in the field of statistical machine learning algorithms. Its originality is to allow to easily express, directly in C++ in a straightforward manner, complex non-linear functions to be optimized.
3）Waffles- C++ Machine Learning。[按：请教联系过作者，好像是一个人在战斗！其基本数据结构是建立在std::vector上的，这一点在速度上我比较担心。代码未考虑并行处理，未考虑硬件平台（这一点shogun做的还算可以）。经常用来产生arff格式的数据放在weka上用]
4）Torch7 provides a Matlab-like environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation
5）SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides methods for linear and nonlinear optimization, in particular evolutionary and gradient-based algorithms, kernel-based learning algorithms and neural networks, and various other machine learning techniques. SHARK serves as a toolbox to support real world applications as well as research in different domains of computational intelligence and machine learning. The sources are compatible with the following platforms: Windows, Solaris, MacOS X, and Linux.
6）Dlib-ml is an open source library, targetedat both engineers and research scientists, which aims to provide a similarly rich environment fordeveloping machine learning software in the C++ language.
7) Eblearn is an object-oriented C++ library that implements various machine learning models, including energy-based learning, gradient-based learning for machine composed of multiple heterogeneous modules. In particular, the library provides a complete set of tools for building, training, and running convolutional networks.
8) Machine Learning Open Source Software :Journal of Machine Learning Research: http://jmlr.csail.mit.edu/mloss/.
9) search in google: c++ site:jmlr.csail.mit.edu filetype:pdf , Machine Learning Toolkit
10) SIGMA: Large-Scale and Parallel Machine-Learning Tool Kit（！shit 源代码找不着了）
11) http://sourceforge.net/directory/science-engineering/ai/machinelearning/os:windows/freshness:recently-updated/[打开这个网址时还是要重新选择条件刷新一下为好！否者页面显示的都不是最近更新过的！]
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12) ELF: ensemble learning framework。特点：c++，监督学习，使用了intel的IPP和MKL，training speed 和accuracy是主要目标。http://elf-project.sourceforge.net/
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13) http://mloss.org/software/ machine learning open sources software。算是一个索引网站吧。
14) http://drwn.anu.edu.au/index.html
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15)SHOGUN: 使用了EIGEN(c++矩阵处理工具库)来处理相关的运算。并有多种(matlab、octave等)平台接口，不过不能在windows中编译和使用(想用的话，的自己改写了！)。EIGEN兼顾了处理速度(并行性)和准确性。所以说阅读shogun和elf中的代码可以学习如何在多核处理器平台上开发机器学习框架和算法库。
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16）
GPUMLib：GPU Machine Learning Library. This library aims to provide machine learning researchers and practitioners with a high performance library by taking advantage of the GPU enormous computational power. The library is developed in C++ and CUDA。[想必对并行处理考虑的比较全面]
17)
MCPN
:Multi-Core optimized Perceptron Network is a high-performance artificial neural network specially designed for workstations with multi-core CPUs, implemented as a shared library and coded in C++。
18）
Accelerated Feature Extraction Tool:
A fast feature extraction software tool for speech analysis and processing. It incorporates standard MFCC, PLP, and TRAPS features. The tool is a specially designed to process very large audio data sets. It uses GPU acceleration if compatible GPU available. CPU SSE intrinsic instruction set is used in cases where no compatible GPU present. The output files are stored in HTK format. The software is developed at Department of Cybernetics at University of West Bohemia in Pilsen.
19）SOPF：还没有出源代码，暂记
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http://machinelearningmastery.com/
http://bbs.sciencenet.cn/home.php?mod=space&uid=240720&do=blog&id=284694 dlib-ml就是建立在kernal基础上的，而这篇博文对kernal的物理意义做出了一般性阐述。除此之外，对研究机器学习总体或哲学框架也说明很多！
http://www.dmoz.org/Computers/Artificial_Intelligence/Machine_Learning/Software/
http://betterexplained.com/articles/linear-algebra-guide/
http://44632186.blog.163.com/blog/static/60862459201251522822754/
http://www.fi.muni.cz/~xsvobod2/misc/lapack/
http://icl.cs.utk.edu/lapack-for-windows/clapack/index.html#libraries
转载： http://blog.csdn.net/genliu777/article/details/7396760
