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program3
MATLAB R2008图形与动画实例教程源程序(MATLAB R2008 graphics and animation tutorial source code examples)
- 2010-08-25 10:25:29下载
- 积分：1
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matlab(2)_Expro
用matab写的一些源程序，对学习matlab的初学者比较有用！(Matab written by some source of learning more useful for beginners in matlab!)
- 2009-05-12 10:12:14下载
- 积分：1
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change
读取字符串，统一转换成小写后，删除相同的字符(Read the string converted to lower case after the reunification, delete the same character)
- 2010-01-26 15:06:30下载
- 积分：1
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hmm.tar
hmm hidden markov model
nice pretty code good luck!!!
- 2010-05-28 23:03:42下载
- 积分：1
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MATLAB
主要讲的是初学者简单的编程，以便帮助初学者学习MATLAB(Is primarily concerned with beginners simple programming, in order to help beginners learn MATLAB
)
- 2013-04-02 12:24:27下载
- 积分：1
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Chapter8
信道容量和编码。介绍了二进制对称信道和加性高斯白噪声信道。(Channel capacity and coding. Describes the binary symmetric channel and additive white Gaussian noise channel.)
- 2013-10-11 15:04:52下载
- 积分：1
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ProblemsTRC
Radio communcation Matlab source codes problems
- 2013-10-24 15:58:55下载
- 积分：1
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GM11
说明： GM(1,1)的程序，要用就下啥，可以预测的，里面有详细的说明，很容易看懂的！(GM (1,1) procedures, to use on the next receivers, and can be predicted, there are detailed explanations, it is easy to read!)
- 2005-09-17 15:27:17下载
- 积分：1
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untitled1
变频调速系统仿真搭建模型 由于观测仿真波形 记录以及分析实验结果(Variable speed control system simulation build the observation model simulation and analysis of experimental results wave record
)
- 2012-05-29 12:32:14下载
- 积分：1
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gpml-matlab-v1.3-2006-09-08
说明： 高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)
- 2020-02-26 20:39:48下载
- 积分：1
