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项目描述

Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, and decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems. For unsupervised learning, milk supports k-means clustering and affinity propagation.

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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.

2013-01-18 07:08
0.5.1

最重要的变化是本征列入的源分布,使牛奶更容易进行编译。另外,此版本添加了子空间投影 k-最近的邻居和 mds_dists 功能。
标签: Minor, bugfix
The most important change is the inclusion of eigen in the source distribution, which makes milk easier to compile. In addition, this release adds subspace projection k-nearest neighbours and mds_dists functionality.

2012-01-17 05:45
0.4.2

是更一致的接口 (学习者忽略它们不能使用的参数和默认的模型支持的 apply_many 方法)。有很多的改进和错误修正。
标签: Minor
Interfaces are more consistent (learners ignore arguments they cannot use and the default model supports the apply_many method). There are many improvements and bugfixes.

2011-08-25 06:52
0.4.0

新特性:并行处理,感知器,纠错输出码。增强功能:设置随机种子随机森林,multi_strategy“参数defaultlearner(),返回值从gridminimise,更快点内核SVMs的,和S形拟合。一个在randomforest修正。
标签: Major
New features: parallel processing, perceptron, and error correcting output codes. Enhancements: setting the random seed in random forests, a 'multi_strategy' parameter for defaultlearner(), a return value from gridminimise, faster dot-kernel SVMs, and sigmoidal fitting. A bugfix in randomforest.

2011-05-11 17:59
0.3.10

新milk.ext.jugparallel模块被添加到接口罐(htt​​p://luispedro.org/software/jug)。这使得它易于并行化,如N - fold交叉验证的东西(根据其自己的处理器上运行的每个倍)或多个kmeans随机启动。增加了一些新的功能:measures.curves.precision_recall,milk.unsupervised.kmeans.select_best.kmeans。一个在SDA和几个小问题棘手的问题在其他地方是固定的。
标签: Minor
The new milk.ext.jugparallel module was added to interface with jug (http://luispedro.org/software/jug). This makes it easy to parallelize things such as n-fold cross validation (each fold runs on its own processor) or multiple kmeans random starts. Some new functions were added: measures.curves.precision_recall, milk.unsupervised.kmeans.select_best.kmeans. A tricky bug in SDA and a few minor issues elsewhere were fixed.

2011-03-16 06:34
0.3.9

许多速度改进。一些错误修正(以gridminimize和树学习)。一些新的实用功能。
标签: Minor
Many speed improvements. Some bugfixes (to gridminimize and tree learning). A few new utility functions.

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