An improved grid search algorithm to optimize SVR for prediction
Y Sun, S Ding, Z Zhang, W Jia - Soft Computing, 2021 - Springer
Parameter optimization is an important step for support vector regression (SVR), since its
prediction performance greatly depends on values of the related parameters. To solve the …
prediction performance greatly depends on values of the related parameters. To solve the …
[图书][B] Robust multivariate analysis
DJ Olive, DJ Olive, Chernyk - 2017 - Springer
Statistics is the science of extracting useful information from data, and a statistical model is
used to provide a useful approximation to some of the important characteristics of the …
used to provide a useful approximation to some of the important characteristics of the …
A geometric framework for outlier detection in high‐dimensional data
M Herrmann, F Pfisterer… - … Reviews: Data Mining and …, 2023 - Wiley Online Library
Outlier or anomaly detection is an important task in data analysis. We discuss the problem
from a geometrical perspective and provide a framework which exploits the metric structure …
from a geometrical perspective and provide a framework which exploits the metric structure …
Machine learning for sensor-based manufacturing processes
The increasing availability of relevant information, events and constraints in the environment
of the modern factories due to deployment of IoT sensor technologies on the production line …
of the modern factories due to deployment of IoT sensor technologies on the production line …
Automated weighted outlier detection technique for multivariate data
SN Thennadil, M Dewar, C Herdsman, A Nordon… - Control Engineering …, 2018 - Elsevier
In the chemical and petrochemical industries, spectroscopy-based online analysers are
becoming common for process monitoring and control applications. A significant challenge …
becoming common for process monitoring and control applications. A significant challenge …
Phase I distribution-free analysis of multivariate data
G Capizzi, G Masarotto - Technometrics, 2017 - Taylor & Francis
In this study, a new distribution-free Phase I control chart for retrospectively monitoring
multivariate data is developed. The suggested approach, based on the multivariate signed …
multivariate data is developed. The suggested approach, based on the multivariate signed …
ResNet-AE for radar signal anomaly detection
D Cheng, Y Fan, S Fang, M Wang, H Liu - Sensors, 2022 - mdpi.com
Radar signal anomaly detection is an effective method to detect potential threat targets.
Given the low Accuracy of the traditional AE model and the complex network of GAN, an …
Given the low Accuracy of the traditional AE model and the complex network of GAN, an …
PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data
Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical
research. However, one source of HD data that has received relatively little attention is …
research. However, one source of HD data that has received relatively little attention is …
Change detection using a texture feature space outlier index from mono-temporal remote sensing images and vector data
D Wei, D Hou, X Zhou, J Chen - Remote Sensing, 2021 - mdpi.com
Multi-temporal remote sensing images are the primary sources for change detection.
However, it is difficult to obtain comparable multi-temporal images at the same season and …
However, it is difficult to obtain comparable multi-temporal images at the same season and …
Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse …
S Zahariah, H Midi - Journal of Applied Statistics, 2023 - Taylor & Francis
The main aim of this paper is to propose a novel method (RMD-MRCD-PCA) of identification
of High Leverage Points (HLPs) in high-dimensional sparse data. It is to address the …
of High Leverage Points (HLPs) in high-dimensional sparse data. It is to address the …