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search
用MATLAB实现图像处理中的连通域搜索，给出了程序和仿真的结果。(Image Processing with MATLAB in the connected domain search procedure is given and simulation results.)
- 2010-06-30 11:13:51下载
- 积分：1
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Powell
说明： 用Matlab编写的用Powell方法求函数极小点程序(Written with the Matlab function using Powell method for solving minimum point procedure)
- 2010-04-07 09:42:38下载
- 积分：1
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OMP
一个很好的MATLAB源代码——OMP，OMP算法的改进之处在于：在分解的每一步对所选择的全部原子进行正交化处理，这使得在精度要求相同的情况下，OMP算法的收敛速度更快。(A good MATLAB source code-- OMP, OMP algorithm improvement lies in: every step of the decomposition of orthogonalization processing all of the selected atoms, which makes the accuracy requirement of the same circumstances, the OMP algorithm s convergence speed is faster.
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- 2013-08-24 12:51:20下载
- 积分：1
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fuzzy-control-system
基于matlab的模糊控制系统 针对智能控制的学习 使用模糊算法 (fuzzy control system that based on the matlab )
- 2011-05-03 08:56:35下载
- 积分：1
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TSAPNaN
如题，时间序列分析相关matlab程序代码，应该比较全了(Relevant matlab code such as the title, time series analysis, all)
- 2013-02-28 10:29:53下载
- 积分：1
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jpegdemo
this program is about jpeg demo
- 2011-12-02 02:55:54下载
- 积分：1
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XieBeniforFCM
xie beni for clustering
- 2013-12-04 22:03:53下载
- 积分：1
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orginal
watermarking with informed embedding
- 2013-12-23 22:55:37下载
- 积分：1
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用遗传算法进行多元函数的优化 GA-for-MOO-master
说明： 用遗传算法进行多元函数的优化，是测试函数与数据集的基本方法(Using genetic algorithm to optimize multivariate function is the basic method of testing function and data set)
- 2019-11-06 10:38:12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
