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23 papers · ranked by Valyu relevance

01
AarXiv2022
An approach to the Gaussian RBF kernels via Fock spaces
Daniel Alpay, Fabrizio Colombo, Kamal Diki, Irene Sabadini
We use methods from the Fock space and Segal–Bargmann theories to prove several results on the Gaussian RBF kernel in complex analysis. The latter is one of the most used kernels in modern machine learning kernel methods, and in support vector machines (SVMs) classification algorithms. Complex analysis techniques allow…
88relevance
02
PPubMed10 Feb 2022
Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study
Junrong Du, Jian Zhang, Laishun Yang, Xuzhi Li + 8 more
'Yangquan Chen' 'Subhas Mukhopadhyay' 'Nunzio Cennamo' 'M. Jamal Deen' 'Junseop Lee' 'Simone Morais'] Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scenarios due to difficulty installation requirements and complex work…
81relevance
03
PPubMed25 Feb 2022
Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective
Mahmood Ahmad, Ramez A. Al-Mansob, Irfan Jamil, Mohammad A. Al-Zubi + 3 more
'Mohanad Muayad Sabri Sabri' 'Arnold C. Alguno' 'Prabir K. Sarker'] The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging…
81relevance
04
PPubMed30 Aug 2024
Implementing Gaussian process modelling in predictive maintenance of mining machineries
Zhixuan Shao, Mustafa Kumral
Mining machinery constitutes essential assets for a mining corporation. Due to economies of scale, technological innovations and stringent quality and safety requirements, the size, complexity, functionality and diversity of industrial machinery have expanded markedly over the last two decades. This growth has…
81relevance
05
AarXiv8 Sep 2024
Cluster-based Random Radial Basis Kernel Function for Hyperspectral Data Classification
Saeid Niazmardi
Classification Authors: ['Saeid Niazmardi'] Kernel-based classification methods, particularly the support vector machine (SVM), are among the most common algorithms for hyperspectral data classification. The Radial Basis function (RBF) kernel has earned great popularity in hyperspectral data classification due to its…
79relevance
06
PPubMed29 Aug 2025
Quantitative Structure–Activity Relationship Study of Cathepsin L Inhibitors as SARS-CoV-2 Therapeutics Using Enhanced SVR with Multiple Kernel Function and PSO
Shaokang Li, Zheng Li, Peijian Zhang, Aili Qu + 2 more
'Jesús Vicente de Julián-Ortiz'] Cathepsin L (CatL) is a critical protease involved in cleaving the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facilitating viral entry into host cells. Inhibition of CatL is essential for preventing SARS-CoV-2 cell entry, making it a potential…
79relevance
07
AarXiv2023
Gaussian RBF-kernels via Fock spaces: quaternionic and several complex variables settings
Antonino De Martino, Kamal Diki
In this paper we study two extensions of the complex-valued Gaussian radial basis function (RBF) kernel and discuss their connections with Fock spaces in two different settings. First, we introduce the quaternonic Gaussian RBF kernel constructed using the theory of slice hyperholomorphic functions. Then, we consider…
78relevance
08
PPubMed2 Feb 2024
Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO Xue et al. 10.3389/fphar.2024.1257253
Haohan Xue, Ruixuan Zhang, Xudong Yan, Ruihan Wang + 1 more
PARP1 is one of six enzymes required for the highly error-prone DNA repair pathway microhomology-mediated end joining (MMEJ) and needs to be inhibited when over-expressed. In order to study the PARP1 inhibitory effect of fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives (FTPDDs) by quantitative…
77relevance
09
PPubMed22 Jul 2024
Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine
Muhammad Tahir, Bu Yude, Tahir Mehmood, Saima Bashir + 2 more
'Muhammad Awais'] Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the…
77relevance
10
AarXiv2023
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels
Fan He, Mingzhen He, Lei Shi, Xiaolin Huang + 1 more
The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function…
76relevance
11
BbioRxiv27 Feb 2023
EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations
Gargi Biswas, Debasish Mukherjee, Nalok Dutta, Prithwi Ghosh + 1 more
Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis (‘classic type functions’ of proteins) or a signal transduction (‘non-classic’), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such…
76relevance
12
BbioRxiv24 Oct 2024
Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis
Alex H. Williams
Centered kernel alignment (CKA) and representational similarity analysis (RSA) of dissimilarity matrices are two popular methods for comparing neural systems in terms of representational geometry. Although they follow a conceptually similar approach, typical implementations of CKA and RSA tend to result in numerically…
75relevance
13
BbioRxiv6 Oct 2023
Non-linear phylogenetic regression using regularized kernels
Ulises Rosas-Puchuri, Aintzane Santaquiteria, Sina Khanmohammadi, Claudia Solís-Lemus + 1 more
Phylogenetic regression is a type of Generalized Least Squares (GLS) method that incorporates a covariance matrix based on the evolutionary relationships between species (i.e., phylogenetic relationships). While this method has found widespread use in hypothesis testing via comparative phylogenetic methods, such as…
73relevance
14
AarXiv3 Jun 2024
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
Fan He, Mingzhen He, Lei Shi, Xiaolin Huang + 1 more
Learning Authors: ['Fan He' 'Mingzhen He' 'Lei Shi' 'Xiaolin Huang' 'Johan A. K. Suykens'] Ridgeless regression has garnered attention among researchers, particularly in light of the "Benign Overfitting" phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless…
73relevance
15
CchemRxiv22 Sep 2025
Learning the One-Electron Reduced Density Matrix at SCF Convergence Thresholds
Authors not listed
Machine learning of the one-electron reduced density matrix (1-RDM) provides a computationally efficient surrogate to conventional electronic structure methods. In this work, we train models that map the electron–nuclear interaction potential to the 1-RDM with such an accuracy that predicted 1-RDMs deviate from fully…
70relevance
16
BbioRxiv24 May 2023
Enhanced Annotation of CD45RA to Distinguish T cell Subsets in Single Cell RNA-seq via Machine Learning
Ran Ran, Douglas K. Brubaker
T cell heterogeneity presents a challenge for accurate cell identification, understanding their inherent plasticity, and characterizing their critical role in adaptive immunity. Immunologists have traditionally employed techniques such as flow cytometry to identify T cell subtypes based on a well-established set of…
70relevance
17
CchemRxiv28 Dec 2025
Accurate and Robust Analysis of Molecular Kinetics with Random Features
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
69relevance
18
CchemRxiv15 Jul 2022
Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes
Souvik Manna, Diptendu Roy, Sandeep Das, Biswarup Pathak
Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important…
67relevance
19
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
62relevance
20
CchemRxiv25 Mar 2024
Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
62relevance
21
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
60relevance
22
CchemRxiv21 Sep 2023
Machine Learning of Reactive Potentials
Yinuo Yang, Shuhao Zhang, Kavindri Ranasinghe, Olexandr Isayev + 1 more
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological and material sciences. The construction and training of MLPs enables fast and accurate simulations and analysis on thermodynamic and kinetic properties. This review focuses on the applications of…
59relevance
23
BbioRxiv15 Dec 2024
A simple neural circuit model explains diverse types of integration kernels in perceptual decision-making
Xuewen Shen, Fangting Li, Bin Min
The ability to accumulate evidence over time for deliberate decision is essential for both humans and animals. Decades of decision-making research have documented various types of integration kernels that characterize how evidence is temporally weighted. While numerous normative models have been proposed to explain…
51relevance

Results for RBF kernel

An approach to the Gaussian RBF kernels via Fock spaces

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

Implementing Gaussian process modelling in predictive maintenance of mining machineries

Cluster-based Random Radial Basis Kernel Function for Hyperspectral Data Classification

Quantitative Structure–Activity Relationship Study of Cathepsin L Inhibitors as SARS-CoV-2 Therapeutics Using Enhanced SVR with Multiple Kernel Function and PSO

Gaussian RBF-kernels via Fock spaces: quaternionic and several complex variables settings

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO Xue et al. 10.3389/fphar.2024.1257253

Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis

Non-linear phylogenetic regression using regularized kernels

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

Learning the One-Electron Reduced Density Matrix at SCF Convergence Thresholds

Enhanced Annotation of CD45RA to Distinguish T cell Subsets in Single Cell RNA-seq via Machine Learning

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning of Reactive Potentials

A simple neural circuit model explains diverse types of integration kernels in perceptual decision-making

An approach to the Gaussian RBF kernels via Fock spaces

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

Implementing Gaussian process modelling in predictive maintenance of mining machineries

Cluster-based Random Radial Basis Kernel Function for Hyperspectral Data Classification

Quantitative Structure–Activity Relationship Study of Cathepsin L Inhibitors as SARS-CoV-2 Therapeutics Using Enhanced SVR with Multiple Kernel Function and PSO

Gaussian RBF-kernels via Fock spaces: quaternionic and several complex variables settings

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO Xue et al. 10.3389/fphar.2024.1257253

Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis

Non-linear phylogenetic regression using regularized kernels

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

Learning the One-Electron Reduced Density Matrix at SCF Convergence Thresholds

Enhanced Annotation of CD45RA to Distinguish T cell Subsets in Single Cell RNA-seq via Machine Learning

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning of Reactive Potentials

A simple neural circuit model explains diverse types of integration kernels in perceptual decision-making