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
上傳時間： 2016-11-27
上傳用戶：凌云御清風(fēng)
The BNL toolbox is a set of Matlab functions for defining and estimating the parameters of a Bayesian network for discrete variables in which the conditional probability tables are specified by logistic Regression models. Logistic Regression can be used to incorporate restrictions on the conditional probabilities and to account for the effect of covariates. Nominal variables are modeled with multinomial logistic Regression, whereas the category probabilities of ordered variables are modeled through a cumulative or adjacent-categories response function. Variables can be observed, partially observed, or hidden.
標(biāo)簽： estimating parameters functions defining
上傳時間： 2014-12-05
上傳用戶：天誠24
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and Regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
標(biāo)簽： foundations The consists sections
上傳時間： 2017-06-22
上傳用戶：lps11188
matlab中使用LM訓(xùn)練方法計算XOR，3-bit Parity，Regression等問題的收斂速度，比較其收斂率。
上傳時間： 2014-01-10
上傳用戶：zhengzg
Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel Regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
標(biāo)簽： Super-resolution Multi-scale Dictionary Single Image for
上傳時間： 2019-03-28
上傳用戶：fullout
Libsvm is a simple, easy-to-use, and efficient software for SVM classification and Regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM Regression, and nu-SVM Regression. It also provides an automatic model selection tool for C-SVM classification.
標(biāo)簽： LibSVM
上傳時間： 2019-06-09
上傳用戶：lyaiqing
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear Regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標(biāo)簽： interpretable-machine-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
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