a high quality statics studying materials, informative and useful
標(biāo)簽： informative materials studying quality
上傳時(shí)間： 2015-10-06
上傳用戶：zwei41
european_swaption_lmm.txt very nice implementation of complicated LMM. informative for QF applications
標(biāo)簽： european_swaption_lmm implementation complicated informative
上傳時(shí)間： 2016-07-04
上傳用戶：zsjinju
there are some newly released Neural Network Example Programs for Character Recognition, which based on Image Processing Toolbox,Neural Network Toolbox in MATLAB, which is quite informative for the beginners in Neural networks applicators
標(biāo)簽： Recognition Character Programs released
上傳時(shí)間： 2015-09-17
上傳用戶：CHINA526
PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
標(biāo)簽： from eliminates PRINCIPLE algorithm
上傳時(shí)間： 2016-11-27
上傳用戶：凌云御清風(fēng)
The salience distance transform incorporates edge strength information into the distance transform. The output is more robust and informative as input for techniques such as snakes
標(biāo)簽： transform distance incorporates information
上傳時(shí)間： 2014-01-11
上傳用戶：zjf3110
Fundamentals of WiMAX was consciously written to appeal to a broad audience, and to be of value to anyone who is interested in the IEEE 802.16e standards or wireless broadband networks more generally. The book contains cutting-edge tutorials on the technical and theoretical under- pinnings to WiMAX that are not available anywhere else, while also providing high-level over- views that will be informative to the casual reader.
標(biāo)簽： Understanding Fundamentals WiMAX of
上傳時(shí)間： 2020-05-27
上傳用戶：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
標(biāo)簽： Convolutional Networks Neural Guide to
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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