c++开源机器学习库（更新中）

1）mlpack is a C++ machine learning library.

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。

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

11) http://sourceforge.net/directory/science-engineering/ai/machinelearning/os:windows/freshness:recently-updated/

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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。

from http://blog.csdn.net/genliu777/article/details/7396760