Article
Chemistry, Multidisciplinary
Lei Qiao, You Cui, Zhining Jia, Kun Xiao, Haonan Su
Summary: Geophysical logging is crucial in the oil/gas industry. Predicting missing well logs is an effective way to reduce exploration costs. This study proposes a method based on the BO-HKELM algorithm, which optimizes model parameters to improve the accuracy and stability of missing well logs prediction.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Jian Li, Yong Liu, Weiping Wang
Summary: This paper proposes a distributed learning method DNystro center dot m with globally-shared Nystro center dot m centers, which improves the limitations of DKRR in processing complicated tasks by utilizing global information. The statistical properties of DNystro center dot m in expectation and in probability are studied, and state-of-the-art results with the minimax optimal learning rates are obtained.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Computer Science, Hardware & Architecture
Yifei Wang, Mert Pilanci
Summary: We propose a fast algorithm for computing the entire ridge regression regularization path in nearly linear time. Our method constructs a basis on which the solution of ridge regression can be computed instantly for any value of the regularization parameter. Consequently, linear models can be tuned via cross-validation or other risk estimation strategies with substantially better efficiency.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Biochemical Research Methods
Bingxing An, Mang Liang, Tianpeng Chang, Xinghai Duan, Lili Du, Lingyang Xu, Lupei Zhang, Xue Gao, Junya Li, Huijiang Gao
Summary: The study introduced a novel cosine kernel-based KRR model, KCRR, for genomic prediction (GP) in breeding programs. KCRR showed stable performance across multiple species, suggesting its potential for diverse genetic architectures. Additionally, a modified genomic similarity matrix called Cosine similarity matrix (CS matrix) was defined, which significantly improved computing efficiency without compromising prediction accuracy when compared to traditional methods like GBLUP. This research presents a promising strategy for future genomic prediction.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Spectroscopy
Rui Zhang, Peng Wang, Jiansheng Chen, Yubing Tian, Jing Gao
Summary: A bloodstain age estimation method based on Raman spectroscopy and chemometrics was developed in this study, which enables quick and accurate extraction of information from bloodstains without damage. Simulated environments with different temperature and humidity were constructed, and bloodstains from three species, including human, were studied. The influence of environmental factors on the variation of Raman spectral peaks during the aging process of bloodstains was analyzed, providing data support for the further development and application of Raman spectroscopy for bloodstain age estimation in actual scenes.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2023)
Article
Automation & Control Systems
Barenya Bikash Hazarika, Deepak Gupta
Summary: This study proposes a novel affinity-based fuzzy kernel ridge regression (AFKRR) model to tackle the class imbalance learning (CIL) problem. Experimental results demonstrate the good performance and efficacy of the proposed AFKRR model.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
J-L Akian, L. Bonnet, H. Owhadi, E. Savin
Summary: This paper introduces algorithms for selecting/designing kernels in Gaussian process regression/kriging surrogate modeling techniques. It presents two classes of algorithms: kernel flow, which selects the best kernel by minimizing the loss of accuracy caused by removing a portion of the dataset, and spectral kernel ridge regression, which selects the best kernel by minimizing the norm of the function to be approximated in the associated RKHS. The effectiveness of both approaches is demonstrated through numerical examples.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Automation & Control Systems
Zejian Liu, Garvesh Raskutti
Summary: This article proposes a simple plug-in kernel ridge regression estimator for nonparametric regression problems with multi-dimensional support and arbitrary mixed-partial derivatives. It provides a non-asymptotic analysis and achieves the optimal rate of convergence in estimating derivatives for certain classes of functions.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Biochemical Research Methods
Chun-Chun Wang, Chi-Chi Zhu, Xing Chen
Summary: MicroRNAs (miRNAs) play important roles in human disease, and identifying SM-miRNA associations is crucial for drug development and treatment. This study proposes EKRRSMMA, a method that combines feature dimensionality reduction and ensemble learning to predict potential SM-miRNA associations. Evaluation and case studies confirm the reliability of EKRRSMMA.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Materials Science, Composites
Mingqing Yuan, Haitao Zhao, Yuehan Xie, Hantao Ren, Li Tian, Zhuoxin Wang, Boming Zhang, Ji 'an Chen
Summary: This paper introduces a method for predicting axial elastic modulus degradation of [0m/90n]s cross-ply laminates using a machine learning model, based on experimental data and some finite element analysis results. The study also discusses the impact of data size on the accuracy of ML prediction. The proposed ML model offers an efficient solution for complex mechanical problems of composites.
COMPOSITES SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Chong Peng, Qian Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng
Summary: In this paper, a novel subspace clustering method named KTRR is proposed for 2D data. It learns representative 2D features and constructs low-dimensional representations simultaneously, enhancing each other. KTRR introduces a 2D kernel to capture nonlinear relationships from data effectively.
PATTERN RECOGNITION
(2021)
Article
Engineering, Electrical & Electronic
Jiahao Xu, Sai Tang, Pengyan Li, Hexu Zhang
Summary: Based on literature review and empirical analysis, it is found that the increase in grain output is mainly attributed to the increase in sown area of grain crops. The use of agricultural fertilizers and the increase in rural electricity consumption are also driving factors. However, the impact of total power of agricultural machinery is limited, and natural disasters have a certain negative impact on food production.
JOURNAL OF SENSORS
(2022)
Article
Engineering, Manufacturing
Heping Chen, John Leclair
Summary: Research on optimizing recipes to reduce dimensional variations in etching processes is critical. A learning method based on Kernel Ridge Regression (KRR) is proposed to generate optimal recipes for multi-input multi-output (MIMO) systems. Experimental data from a dry etch process were used to demonstrate the effectiveness of the proposed method in exploring optimal recipes for MIMO systems.
JOURNAL OF MANUFACTURING PROCESSES
(2021)
Article
Engineering, Chemical
Amin Shahsavar, Mehdi Jamei, Masoud Karbasi
Summary: Experiments in the study focus on investigating the impact of shear rate, nanoparticle concentration, and magnetic field induction on the viscosity of water-Fe3O4 magnetic nanofluid (MNF). Results show a complex relationship between these factors and viscosity. A novel machine learning model, Grid-KRR, is developed for accurately predicting viscosity based on input features such as nanoparticle volume fraction, shear rate, and magnitude of external magnetic field. Performance evaluation indicates that the Grid-KRR model outperforms other models like Random Forest and Gene expression programming.
Article
Automation & Control Systems
Emilio Tanowe Maddalena, Paul Scharnhorst, Colin N. Jones
Summary: This paper discusses the problem of reconstructing a function from noise-corrupted samples, analyzing two kernel algorithms- kernel ridge regression and epsilon-support vector regression. By establishing finite-sample error bounds and providing numerical examples, the connection between these algorithms and Gaussian processes is explored, aiming to bridge the gap between non-parametric kernel learning and system identification for robust control.
