Kernel regression
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위키데이터
- ID : Q1739319
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- In statistics, Kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable.[1]
- the function npreg of the np package can perform kernel regression.[1]
- This leads to the technique known as kernel regression.[2]
- This gives us a mathematical justification for using kernel regression in cases where it is possible to do so.[2]
- On the down side, since we need to calculate the Gram matrix, kernel regression does not scale well – for large datasets turning to neural networks is a better idea.[2]
- Bandwidth in kernel regression is called the smoothing parameter because it controls variance and bias in the output.[3]
- In this example, a kernel regression model is developed to predict river flow from catchment area.[3]
- In this example, a bandwidth value of 10 is used to explain kernel regression.[3]
- The fundamental calculation behind kernel regression is to estimate weighted sum of all observed y values for a given predictor value, xi.[3]
- Let’s see an application of multivariate kernel regression for the wine.csv dataset.[4]
- Using only the blue data points, Gaussian Kernel Regression arrives at the approximated function given by the red line.[5]
- The above equation is the formula for what is more broadly known as Kernel Regression.[5]
- In robust nonparametric kernel regression context, we prescribe method to select trimming parameter and bandwidth.[6]
- Kernel regression is an estimation technique to fit your data.[7]
- The idea of kernel regression is putting a set of identical weighted function called Kernel local to each observational data point.[7]
- In Kernel regression, what you do is to put a kernel (a kind of bump function) to each point of your X data.[7]
- A SAS programmer recently asked me how to compute a kernel regression in SAS.[8]
- "What is loess regression" and "Loess regression in SAS/IML" and was trying to implement a kernel regression in SAS/IML as part of a larger analysis.[8]
- This article explains how to create a basic kernel regression analysis in SAS.[8]
- A kernel regression smoother is useful when smoothing data that do not appear to have a simple parametric relationship.[8]
- Kernel regression is a modeling tool which belongs to the family of smoothing methods.[9]
- Unlike linear regression which is both used to explain phenomena and for prediction (understanding a phenomenon to be able to predict it afterwards), Kernel regression is mostly used for prediction.[9]
- In this paper, we first review the latest developments of sparse metric learning and kernel regression.[10]
- Then a novel kernel regression method involving sparse metric learning, which is called kernel regression with sparse metric learning (KR_SML), is proposed.[10]
- The sparse kernel regression model is established by enforcing a mixed (2,1)-norm regularization over the metric matrix.[10]
- Our work is the first to combine kernel regression with sparse metric learning.[10]
- I am using kernel regression for build a relationship between independent and dependent variables in development period data.[11]
- Train a default Gaussian kernel regression model with the standardized predictors.[12]
- Below you will find a range of commented examples intended to help you become familiar with applied nonparametric kernel regression in R/RStudio using the np package.[13]
- As in kernel density estimation, kernel regression involves choosing the kernel function and the bandwidth parameter.[14]
소스
- ↑ 1.0 1.1 Kernel regression
- ↑ 2.0 2.1 2.2 Advanced Machine Learning: Basics and Kernel Regression
- ↑ 3.0 3.1 3.2 3.3 Kernel Regression — with example and code
- ↑ Notes for Nonparametric Statistics
- ↑ 5.0 5.1 Kernel Regression · Chris McCormick
- ↑ Robust nonparametric kernel regression estimator
- ↑ 7.0 7.1 7.2 Kernel Regression
- ↑ 8.0 8.1 8.2 8.3 Kernel regression in SAS
- ↑ 9.0 9.1 Nonparametric regression (Kernel and Lowess)
- ↑ 10.0 10.1 10.2 10.3 Kernel regression with sparse metric learning
- ↑ Validation of Kernel Regression
- ↑ Fit Gaussian kernel regression model using random feature expansion
- ↑ Kernel regression examples using np
- ↑ 6.2 Kernel Regression
메타데이터
위키데이터
- ID : Q1739319
Spacy 패턴 목록
- [{'LOWER': 'kernel'}, {'LEMMA': 'regression'}]