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Matlab_instruction
Matlab的函数及指令提供给大家查询maltab指令的方便
- 2011-01-05 10:02:40下载
- 积分：1
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2
说明： DGM model generate for serial robot
- 2008-12-09 06:57:18下载
- 积分：1
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30
说明： 智能算法三十例，matlab版，希望对大家有用，真心要好好学习一下(this is very good and helpfull to you!)
- 2014-10-25 09:19:37下载
- 积分：1
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123
matlab反演算法与优化，是反演算法的集锦，附有多套基本反演算法(matlab inversion algorithm and optimization, is the inversion algorithm highlights, with several sets of basic inversion algorithm)
- 2017-03-27 22:24:42下载
- 积分：1
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proj_2
Opencv code for Sobel edge detection
- 2013-11-24 22:00:49下载
- 积分：1
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边缘检测特征提取
说明： 边缘检测是图形图像处理、计算机视觉和机器视觉中的一个基本工具，通常用于特征提取和特征检测，旨在检测一张数字图像中有明显变化的边缘或者不连续的区域，在一维空间中，类似的操作被称作步长检测（step detection）。(Edge detection is a basic tool in graphics and image processing, computer vision and machine vision. It is usually used for feature extraction and feature detection. It aims to detect the edge or discontinuous area with obvious changes in a digital image. In one-dimensional space, similar operations are called step detection)
- 2020-04-08 14:50:25下载
- 积分：1
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ica1
independent component analysis (ICA)
- 2012-10-02 11:06:51下载
- 积分：1
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source_matlab
利用微分方程的相关算法检测仪表盘的matlab程序(Detection of differential equations of the correlation algorithm matlab program dashboard)
- 2010-11-23 14:42:24下载
- 积分：1
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Image-Processing-with-Matlab
Programming with matlab. Filetype: djvu
- 2012-04-12 23:51:09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
