irrelevant variablessimulationsWe show that when estimating a non-parametric regression model, the k-nearest-neighbour non-parametric estimation method has the ability to remove irrelevant variables provided one uses a product weight function with a vector of smoothing parameters, and the least-squares ...
K-Nearest Neighbor (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model. Methods. LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-...
Vieu. k-nearest neighbour method in functional nonpara- metric regression. J. Nonparametr. Stat., 21(4):453-469, 2009.Burba F, Ferraty F, Vieu P (2009) K-nearest neighbour method in functional nonparametric regression. J Nonparametric Stat 21:453–469 MATH MathSciNet...
Pre- and post-COPs are analyzed for a chiller system retrofit.kNN regression searcheskneighbours from pre-operating conditions.k=5 minimizes the test error in cross validation in clustering cooling capacity, outdoor temperature and relative humidity.COP improves by 0.01–88.30% for 79.63% of post-...
2D-QSAR model developed using partial least square regression approach. Negative logarithmic value of (–PMIC) was taken as dependent variable and T_N_N_4, T_2_C_1, T_T_C_4, T_T_S_7 T_2_C_1, ChiV3, T_O_O_7 was taken as independent varable. The analysis resulted in the ...
Souza Filho and Lall [31] used a modified Euclidean distance formulation based on coefficients from a fitted multiple linear regression model among the predictors and predictands. These formulations are discussed in detail later in the paper. The basic concept of expressing the contribution of each...
2020 Spring Fudan University Data Mining Course HW by prof. Zhu Xuening. 复旦大学大数据学院2020年春季课程-数据挖掘(DATA620007)包含数据挖掘算法模型:Linear Regression Model、Logistic Regression Model、Linear Discriminant Analysis、K-Nearest Neighbour、N
This study applies the k nearest neighbour (kNN) regression to ascertain optimal operating strategies for a chiller system, hence lowering its carbon emissions. First, 19 operating variables were identified for the regression of the coefficient of performance (COP)鈥攖he total cooling capacity divided...
nonparametric regressionkNN estimatorrate of convergencerandom bandwidthThe aim of this article is to study the k-nearest neighbour (kNN) method in nonparametric functional regression. We present asymptotic properties of the kNN kernel estimator: the almost-complete convergence and its rate. Then, we ...
In the case when past returns were used, k-NN based methods were the most consistently profitable, followed by the linear ridge regression and quadratic ridge regression. Neural networks, while being able to profit on some of the time series, did not achieve profit on most of the others. ...