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项目描述

SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.

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Information regarding Project Releases and Project Resources. Note that the information here is a quote from Freecode.com page, and the downloads themselves may not be hosted on OSDN.

2012-09-06 10:24
2.0.0

这主要更新添加了许多的改进、 新的功能和错误修正。它包括一切，已进行了之前和期间谷歌代码 2012 年夏天。学生有实施各项新功能，如学习、 高斯过程，潜在变量支持向量机的结构化输出 （和有系统的输出学习），内核复制空格中的统计检验，各种多任务学习算法和各种不同的可用性改进，仅举几例。
标签: Major feature enhancements
This major update adds many improvements, new features, and bugfixes. It includes everything which has been carried out before and during the Google Summer of Code 2012. Students have implemented various new features such as structured output learning, gaussian processes, latent variable SVM (and structured output learning), statistical tests in kernel reproducing spaces, various multitask learning algorithms, and various usability improvements, to name a few.

2011-12-13 13:50
1.1.0

此版本引入 '转换器' 的概念使您能够构建嵌入的任意功能。它还包括维数减少工具包中的几个新维度减少技术和显著的性能改进。其他改进包括重大汇编提速，各种错误修正模块化接口和算法，和改进这个软件、 Mac OS X 和铿锵 + + 的兼容性。Github 的问题现在用于跟踪 bug 和问题。
标签: Feature Enhancements, cleanups, Bugfixes
This release introduced the concept of 'converters', which enables you to construct embeddings of arbitrary features. It also includes a few new dimension reduction techniques and significant performance improvements in the dimensionality reduction toolkit. Other improvements include a significant compilation speed-up, various bugfixes for modular interfaces and algorithms, and improved Cygwin, Mac OS X, and clang++ compatibility. Github Issues is now used for tracking bugs and issues.

2011-09-01 12:16
1.0.0

本次发布的新的语言，包括Java和C＃，Ruby中，Lua中，一个模式的选择框架，许多降维技术，高斯混合模型的估计，和一个全面的在线学习框架的接口。
标签: Major feature enhancements, Bugfixes, Code cleanup
This release features interfaces to
new languages including Java, C#, Ruby, and Lua, a model selection framework, many dimension reduction techniques, Gaussian Mixture Model estimation, and a full-fledged online learning framework.

2010-12-07 23:55
0.10.0

这是一个重大的内部用户可见的变化，而且很多新的版本。首先，它现在提供的申请数目（在应用程序文件夹）和所有的数据集正处在一个单独的压缩文件中。对于用户来说，最有趣，最重要的特点是序列化的支持。现在人们可以转储到磁盘的任何对象，并加载它幕府以后。支持的格式包括系列化。HDF5转，ASCII码。JSON的，XML格式，和Python版本1和2泡菜。
This is a major new release with lots of internal but also user visible changes. First of all, it now includes a number of applications (in the applications folder) and all the data sets are now contained in a separate tarball. For the user, the most interesting and important feature is serialization support. One can now dump any shogun object to disk and load it later on. Supported serialization formats include .hdf5, ascii, .json, XML formats, and Python pickle version 1 and 2.

2010-05-31 23:14
0.9.3

本新闻稿包含一些增强功能，清理和错误修正。一个新的字符串内核和多级编号MKL的得到执行。支持的python - dbg的加入。花车现接受为定义内核，现在可以比的大小4GB的投入。 Python的安装使用的distutils现在。静态链接已修复，以及稀疏add_to_normal函数线性内核。
标签: Code cleanup, Bugfixes, Feature Enhancements
This release contains several enhancements, cleanups, and bugfixes. A number of new string kernels and multi-class MKL were implemented. Support for python-dbg was added. Floats are now accepted as input for custom kernels that now can be more than 4GB in size. Python installation uses distutils now. Static linking has been fixed, as well as the sparse linear kernels add_to_normal function.

Project Resources

Project Page 下载: bzip2 下载: Debian Package (deb) 首页 下载: List 下载: Bugtracker