Yongho Jeon

Artificial intelligence technology is rapidly developing with the improvement of computer performance and the development of various algorithms, and research using artificial intelligence technology is being actively conducted in the field of manufacturing technology. In the field of welding, research on arc welding quality prediction using artificial neural network algorithms (ANN) was mainly conducted in the early stages. Since then, in the case of arc welding quality prediction using a deep n

Abstract We consider interval‐valued data that frequently appear with advanced technologies in current data collection processes. Interval‐valued data refer to the data that are observed as ranges instead of single values. In the last decade, several approaches to the regression analysis of interval‐valued data have been introduced, but little work has been done on relevant statistical inferences concerning the regression model. In this paper, we propose a new approach to fit a linear regression

Summary Functional linear discriminant analysis provides a simple yet efficient method for classification, with the possibility of achieving perfect classification. Several methods have been proposed in the literature that mostly address the dimensionality of the problem. On the other hand, there is growing interest in interpretability of the analysis, which favours a simple and sparse solution. In this paper we propose a new approach that incorporates a type of sparsity that identifies nonzero

The log-density functional ANOVA model provides a powerful framework for the es-timation and interpretation of high dimensional densities. Existing methods for fitting such a model require repeated numerical integration of high dimensional functions, and are infeasible in problems of dimension larger than four. We propose a new method for fitting the log-density ANOVA model based on a penalized M-estimation formu-lation with a novel loss function. Solving the penalized M-estimation problem does

Penalized likelihood density estimation provides an effective approach to the nonparametric fitting of graphical models, with conditional independence struc- tures characterized via selective term elimination in functional ANOVA decomposi- tions of the log density. A bottleneck in the approach has been the cost of numerical integration, which has limited its application to low-dimensional problems. In Jeon and Lin (2006), a reformulation was proposed to replace multi-dimensional inte- grals by s

This article concerns datasets in which variables are in the form of intervals, which are obtained by aggregating information about variables from a larger dataset. We propose to view the observed set of hyper-rectangles as an empirical histogram, and to use a Gaussian kernel type estimator to approximate its underlying distribution in a nonparametric way. We apply this idea to both univariate density estimation and regression problems. Unlike many existing methods used in regression analysis, t

Assessment of stenosis degree and plaque type using CCTA provided additional prognostic value over CACS and FRS to risk stratify stroke patients without prior history of CAD better.

Abstract We propose new discrimination methods for classification of high dimension, low sample size (HDLSS) data that regularize the degree of data piling. The within-class scatter of the HDLSS data, when projected onto a low-dimensional discriminant subspace, can be selected to be arbitrarily small. Using this fact, we develop two different ways of tuning the amount of within-class scatter, or equivalently, the degree of data piling. In the first approach, we consider a linear path connecting

There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We

In multi-class discrimination with high-dimensional data, identifying a lower-dimensional subspace with maximum class separation is crucial. We propose a new optimization criterion for finding such a discriminant subspace, which is the ratio of two traces: the trace of between-class scatter matrix and the trace of within-class scatter matrix. Since this problem is not well-defined for high-dimensional data, we propose to regularize the within trace and maximize the between trace. A careful inves