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Probabilistic prediction with locally weighted jackknife predictive system
Probabilistic predictions for regression problems are more popular than point predictions and interval predictions, since they contain more...
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Super-Resolution Reconstruction Based on Kernel Regression Method
Kernel regression method is one of the non-parametric nonlinear regression estimation methods. The univariate regression function is estimated by...
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Geographically Weighted Sparse Group Lasso: Local and Global Variable Selections for GWR
This paper deals with the variable selection problem in geographically weighted regression (GWR). GWR is a local estimation method that continuously...
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Improving the Predictive Ability of Radiomics-Based Regression Survival Models Through Incorporating Multiple Regions of Interest
Radiomic features, numeric values extracted from a region of interest (ROI) in medical images, can be used to train prognostic models for various...
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Simulation of English part-of-speech classification based on artificial intelligence and additive logistic regression
English part-of-speech classification technology is a technology that can process text data, can effectively solve the problem of messy data in text...
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Geographical Weighted Regression Approach: A Case Study on Covid-19 in India
The pandemic disease termed Covid-19 has been increasing in every corner of the geosphere, leaving none untouched. The spread has been unimaginable...
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Survey on Deep Fuzzy Systems in Regression Applications: A View on Interpretability
Deep learning (DL) has captured the attention of the community with an increasing number of recent papers in regression applications, including...
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Bayesian Meta Regression
This work extends Bayesian regression as an adaptive that augmented by deep neural networks (the probabilistic encoder) to obtain the posterior...
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Machining accuracy reliability optimization of three-axis CNC machine tools using doubly-weighted vector projection response surface method
The reasonable allocation of geometric errors of machine tools can improve their machining accuracy reliability (MAR). However, due to the complexity...
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A Nested Differential Evolution Algorithm for Optimal Designs of Quantile Regression Models
The Differential Evolution (DE) is an influential heuristic algorithm effective in attaining global optimization of any real vector-valued function....
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Markdown Optimization with Generalized Weighted Least Squares Estimation
Retailers increasingly apply price markdowns for their seasonal products. Efficiency of these markdown applications is driven by the accuracy of...
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Penalized Estimation of a Finite Mixture of Linear Regression Models
Finite mixtures of linear regressions are often used in practice in order to classify a set of observations and/or explain an unobserved...
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Predict Total Sediment Load Using Standalone and Ensemble Machine Learning Models
Sediment load includes bed and suspended loads. Bed load is sediment on a river’s bottom, while a suspended load is sediment floating in water...
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Super-Resolution MRI Using Fractional Order Kernel Regression and Total Variation
In this paper, we adopt a kernel regression approach with steering kernel estimation using fractional order gradients and a Taylor series...
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A novel range prediction model using gradient descent optimization and regression techniques
Predictive models learn relationships between dependent and independent features of a dataset to forecast future outcomes. The point forecasting...
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Decentralized Sparse Gaussian Process Regression with Event-Triggered Adaptive Inducing Points
In this paper, we present a decentralized sparse Gaussian process regression (DSGPR) model with event-triggered, adaptive inducing points. We address...
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DNS-Based Turbulent Closures for Sediment Transport Using Symbolic Regression
This work aims to improve the turbulence modeling in RANS simulations for particle-laden flows. Using DNS data as reference, the errors of the model...
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Laplace Transformation of Eigen Maps of Locally Preserving Projection (LE-LPP) Technique and Time Complexity
K Nearest Neighbour (k-NN) is one of the most common machine learning algorithms; however, it frequently fails to operate well due to an incorrect...
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Three-Layer Weighted Fuzzy Support Vector Regressions for Emotional Intention Understanding
A three-layer weighted fuzzy support vector regression (TLWFSVR)Three-Layer Weighted Fuzzy Support Vector Regression (TLWFSVR) model is proposed for...
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No-reference image quality assessment with multi-scale weighted residuals and channel attention mechanism
With the rapid development of deep learning, no-reference image quality assessment (NR-IQA) based on convolutional neural network (CNN) plays an...
