-
rgb2hsv-color-space-exchange
为了避免直接对rgb图像处理时出现颜色失真，常把彩色图像由rgb空间转为hsv空间，完成相应处理后，再逆变换至rgb空间。(In order to avoid image processing directly on the rgb color distortion occurs, often the color image from the rgb space into hsv space to complete the appropriate treatment, and then inverse transform to the rgb space.)
- 2010-12-18 10:10:32下载
- 积分：1
-
Trade_Classification
通过ｂｉｄ ，ａｓｋ的分布，用以计算当前市场的交易活跃程度。可以分别统计出卖方触发单和买方触发单(Through the distribution of the bid, ask, for the current market trading activity. Respectively statistics triggered single and buyer seller trigger single)
- 2012-12-28 18:18:03下载
- 积分：1
-
untitled2
双馈风力电机模型，可以再修改，dq坐标下模型(Doubly-fed wind-electric model can be modified, dq model coordinates)
- 2009-07-09 16:45:32下载
- 积分：1
-
matlabprogrammes
说明： 本程序是关于FIR和IIR滤波器的设计现(the procedures of FIR and IIR filter design is)
- 2006-02-22 21:00:23下载
- 积分：1
-
STFT
chirp信号的短时傅里叶变换源代码，雷达、信号处理等领域，可实现对chirp信号的短时傅里叶变换(short-time Fourier transform source code field chirp signals, radar, signal processing can be realized on short-time Fourier transform chirp signal)
- 2020-06-26 14:00:02下载
- 积分：1
-
1001-0548(2012)02-0208-04
宽带CIC抽取滤波器的一种改进方法1001-0548(2012)02-0208-04(Improved Method for Wideband CIC Decimation Filters
)
- 2014-02-01 23:32:08下载
- 积分：1
-
svr
为支持向量机代码，源代码为matlab实现，可以做聚类问题(To support vector machine codes)
- 2021-05-14 10:30:02下载
- 积分：1
-
doKmeans_movingWindow
image segmentation by using k means clustering algorithm
- 2011-07-19 12:31:20下载
- 积分：1
-
SARBP
SAR成像，后向投影算法（BP）的简单介绍及matlab实现(back projection)
- 2020-08-13 18:48:27下载
- 积分：1
-
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
