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Gradient-based kernel variable selection for support vector hazards machine
This study aims to improve the predictive performance for the event time through the machine learning model and find informative variables in the...
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Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis
Kernel methods are often used for nonlinear regression and classification in statistics and machine learning because they are computationally cheap...
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Kernel Linear Model
The kernel concept was introduced into the field of pattern recognition by (Aizerman et al. 1964).
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Machine learning embedded EM algorithms for semiparametric mixture regression models
In this article, we propose two machine learning embedded algorithms for a class of semiparametric mixture models, where the mixing proportions and...
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Nonparametric binary regression models with spherical predictors based on the random forests kernel
Spherical data arise widely in various settings. Spherical statistics is an analysis of data on a unit hyper-spherical domain. In this paper, we...
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Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data
Imputation is a popular technique for handling missing data. We address a nonparametric imputation using the regularized M-estimation techniques in...
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Rates of the Strong Uniform Consistency for the Kernel-Type Regression Function Estimators with General Kernels on Manifolds
AbstractIn the present paper, we develop strong uniform consistency results for the generic kernel (including the kernel density estimator) on...
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Kernel density estimation by stagewise algorithm with a simple dictionary
This study proposes multivariate kernel density estimation by stagewise minimization algorithm based on U -divergence and a simple dictionary. The...
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Kernel regression for estimating regression function and its derivatives with unknown error correlations
In practice, it is common that errors are correlated in the nonparametric regression model. Although many methods have been developed for addressing...
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Fast quantile regression in reproducing kernel Hilbert space
In literature, the idea of kernel machine was introduced to quantile regression, resulting kernel quantile regression (KQR) model, which is capable...
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Constrained clustering and multiple kernel learning without pairwise constraint relaxation
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics...
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Prediction of Forest Fire Risk for Artillery Military Training using Weighted Support Vector Machine for Imbalanced Data
Since the 1953 truce, the Republic of Korea Army (ROKA) has regularly conducted artillery training, posing a risk of wildfires — a threat to both the...
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Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification
We consider predictions in longitudinal studies, and investigate the well known statistical mixed-effects model, piecewise linear mixed-effects model...
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Machine learning and the James–Stein estimator
It is now 62 years since the publication of James and Stein’s seminal article on the estimation of a multivariate normal mean vector. The paper made...
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Outlier detection in non-elliptical data by kernel MRCD
The minimum regularized covariance determinant method (MRCD) is a robust estimator for multivariate location and scatter, which detects outliers by...
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The Statistics of Machine Learning
This chapter offers a general introduction to the statistics of Machine Learning (ML) and constitutes the basics to get through the next chapters of...
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On the Robustness of Kernel-Based Pairwise Learning
It is shown that many results on the statistical robustness of kernel-based pairwise learning can be derived under basically no assumptions on the...
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Enhancing Multilevel Models Through Supervised Machine Learning
Clustered data are common in various fields, such as social sciences (multiple individual measurements) and machine learning (city-wise weather...
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Discriminant Analysis, Nearest Neighbor, and Support Vector Machine
This chapter covers three related machine learning techniques: discriminant analysis (DA), support vector machineSupport vector machine (SVM), and...
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Exploring Machine Learning
This chapter introduces the best machine learning methods and specifies the main differences between supervised and unsupervised machine learning. It...