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26 papers · ranked by Valyu relevance

01
AarXiv2023
Kernel Learning by quantum annealer
Yasushi Hasegawa, Hiroki Oshiyama, Masayuki Ohzeki
The Boltzmann machine is one of the various applications using quantum annealer. We propose an application of the Boltzmann machine to the kernel matrix used in various machine-learning techniques. We focus on the fact that shift-invariant kernel functions can be expressed in terms of the expected value of a spectral…
83relevance
02
PPubMed13 Apr 2022
Weighted <i> p </i> -norm distance t kernel SVM classification algorithm based on improved polarization
Wenbo Liu, Shengnan Liang, Xiwen Qin
The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM. To improve the applicability and classification prediction effect of SVM in different areas, in this…
82relevance
03
PPubMed14 Jun 2024
Deep Kernel learning for reaction outcome prediction and optimization
Sukriti Singh, José Miguel Hernández-Lobato
Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. Gaussian processes (GPs), on the other hand, provide reliable…
81relevance
04
PPubMed23 Nov 2024
Supervised multiple kernel learning approaches for multi-omics data integration
Mitja Briscik, Gabriele Tazza, László Vidács, Marie-Agnès Dillies + 1 more
'Sébastien Déjean'] Background Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid…
81relevance
05
BbioRxiv16 Oct 2021
Kernel-based Nonlinear Manifold Learning for EEG Channel Selection with Application to Alzheimer’s Disease
Rajintha Gunawardena, Ptolemaios G. Sarrigiannis, Daniel J. Blackburn, Fei He
For the characterisation and diagnosis of neurological disorders, dynamical, causal and crossfrequency coupling analysis using the EEG has gained considerable attention. Due to high computational costs in implementing some of these methods, the selection of important EEG channels is crucial. The channel selection…
79relevance
06
AarXiv15 Jul 2025
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Kilic, Afra, Kim Batselier
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model complexity hyperparameters like tensor rank…
79relevance
07
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…
78relevance
08
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…
78relevance
09
BbioRxiv25 Jun 2024
Prediction of protein functions using Semantic Based Regularization
Giovanna Maria Dimitri
In this work, done in collaboration with Prof. Michelangelo Diligenti (department of Engineering and Mathematics, University of Siena) we present the use of Semantic Based Regularization Kernel based machine learning method to predict protein function. We initially build the protein functions ontology, given an initial…
77relevance
10
PPubMed12 Jan 2023
Analysis of Kernel Matrices via the von Neumann Entropy and Its Relation to RVM Performances
Lluís A. Belanche-Muñoz, Małgorzata Wiejacha, Jose C. Principe
Kernel methods have played a major role in the last two decades in the modeling and visualization of complex problems in data science. The choice of kernel function remains an open research area and the reasons why some kernels perform better than others are not yet understood. Moreover, the high computational costs of…
75relevance
11
AarXiv1 Aug 2024
Learning to Embed Distributions via Maximum Kernel Entropy
Oleksii Kachaiev, Stefano Recanatesi
Empirical data can often be considered as samples from a set of probability distributions. Kernel methods have emerged as a natural approach for learning to classify these distributions. Although numerous kernels between distributions have been proposed, applying kernel methods to distribution regression tasks remains…
75relevance
12
BbioRxiv30 Sep 2022
Learning with phenotypic similarity improves the prediction of functional effects of missense variants in voltage-gated sodium channels
Christian Malte Boßelmann, Ulrike B.S. Hedrich, Holger Lerche, Nico Pfeifer
Missense variants in genes encoding voltage-gated sodium channels are associated with a spectrum of severe diseases affecting neuronal and muscle cells, the so-called sodium channelopathies. Variant effects on the biophysical function of the channel correlate with clinical features and can in most cases be categorized…
74relevance
13
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…
74relevance
14
AarXiv14 Mar 2024
Multi-Layer Kernel Machines: Fast and Optimal Nonparametric Regression with Uncertainty Quantification
Xiaowu Dai, Huiying Zhong
with Uncertainty Quantification Authors: ['Xiaowu Dai' 'Huiying Zhong'] Kernel ridge regression (KRR) is widely used for nonparametric regression over reproducing kernel Hilbert spaces. It offers powerful modeling capabilities at the cost of significant computational costs, which typically require O(n 3 ) computational…
74relevance
15
PPubMed22 Oct 2022
Transfer learning: a friendly introduction
Asmaul Hosna, Ethel Merry, Jigmey Gyalmo, Zulfikar Alom + 2 more
'Mohammad Abdul Azim'] Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML…
74relevance
16
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
73relevance
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…
72relevance
18
AarXiv18 Jul 2024
Enhancing Quantum Machine Learning: The Power of Non-Linear Optical Reproducing Kernels
Shahram Dehdashti, Prayag Tiwari, Kareem H. El Safty, Peter Bruza + 1 more
Reproducing Kernels Authors: ['Shahram Dehdashti' 'Prayag Tiwari' 'Kareem H. El Safty' 'Peter Bruza' 'Janis Nötzel'] Amidst the array of quantum machine learning algorithms, the quantum kernel method has emerged as a focal point, primarily owing to its compatibility with noisy intermediate-scale quantum devices and its…
72relevance
19
AarXiv2024
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
Mehmet Şahin, Benjamin C. B. Symons, Pushpak Pati, Fayyaz Minhas + 4 more
'D. A. Millar' 'Maria Gabrani' 'Jan Lukas Robertus' 'Stefano Mensa'] Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore…
71relevance
20
CchemRxiv15 Jun 2022
Krein Support Vector Machine Classification of Antimicrobial Peptides
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
71relevance
21
BbioRxiv8 Feb 2025
An ensemble learning method for joint kernel association testing and principal component analysis on multiple kernels
Hyunwook Koh
In high-dimensional omics studies, researchers often conduct kernel association testing to power-fully detect the relationship of the genetic or microbial composition with human health or disease. Especially, in human microbiome studies, its dimension reduction analysis follows to visually represent complex microbiome…
70relevance
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…
69relevance
23
PPubMed2 Jan 2026
Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters
Lauri Seppäläinen, Jakub Kubečka, Jonas Elm, Kai R. Puolamäki
Machine Learning Modeling of Atmospheric Molecular Clusters Authors: Lauri Seppäläinen, Jakub Kubečka, Jonas Elm, Kai R. Puolamäki Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modeling: the formation of new aerosol particles. While…
69relevance
24
CchemRxiv28 Aug 2025
An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction
Authors not listed
We adapted an existing approach to identifying stabilisable crystal structures from prediction sets - the Generalised Convex Hull (GCH) - to improve its application to molecular crystal structures. This was achieved by modifying the Smooth Overlap of Atomic Positions (SOAP) kernel to define the similarity of molecular…
67relevance
25
BbioRxiv22 Aug 2023
Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection
Prakash Chourasia, Taslim Murad, Sarwan Ali, Murray Patterson
The genetic code for many different proteins can be found in biological sequencing data, which offers vital insight into the genetic evolution of viruses. While machine learning approaches are becoming increasingly popular for many “Big Data” situations, they have made little progress in comprehending the nature of…
67relevance
26
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…
62relevance

Results for Kernel learning

Kernel Learning by quantum annealer

Weighted <i> p </i> -norm distance t kernel SVM classification algorithm based on improved polarization

Deep Kernel learning for reaction outcome prediction and optimization

Supervised multiple kernel learning approaches for multi-omics data integration

Kernel-based Nonlinear Manifold Learning for EEG Channel Selection with Application to Alzheimer’s Disease

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels

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

Prediction of protein functions using Semantic Based Regularization

Analysis of Kernel Matrices via the von Neumann Entropy and Its Relation to RVM Performances

Learning to Embed Distributions via Maximum Kernel Entropy

Learning with phenotypic similarity improves the prediction of functional effects of missense variants in voltage-gated sodium channels

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

Multi-Layer Kernel Machines: Fast and Optimal Nonparametric Regression with Uncertainty Quantification

Transfer learning: a friendly introduction

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

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Enhancing Quantum Machine Learning: The Power of Non-Linear Optical Reproducing Kernels

Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training

Krein Support Vector Machine Classification of Antimicrobial Peptides

An ensemble learning method for joint kernel association testing and principal component analysis on multiple kernels

Machine Learning of Reactive Potentials

Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction

Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection

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

Kernel Learning by quantum annealer

Weighted <i> p </i> -norm distance t kernel SVM classification algorithm based on improved polarization

Deep Kernel learning for reaction outcome prediction and optimization

Supervised multiple kernel learning approaches for multi-omics data integration

Kernel-based Nonlinear Manifold Learning for EEG Channel Selection with Application to Alzheimer’s Disease

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels

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

Prediction of protein functions using Semantic Based Regularization

Analysis of Kernel Matrices via the von Neumann Entropy and Its Relation to RVM Performances

Learning to Embed Distributions via Maximum Kernel Entropy

Learning with phenotypic similarity improves the prediction of functional effects of missense variants in voltage-gated sodium channels

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

Multi-Layer Kernel Machines: Fast and Optimal Nonparametric Regression with Uncertainty Quantification

Transfer learning: a friendly introduction

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

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Enhancing Quantum Machine Learning: The Power of Non-Linear Optical Reproducing Kernels

Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training

Krein Support Vector Machine Classification of Antimicrobial Peptides

An ensemble learning method for joint kernel association testing and principal component analysis on multiple kernels

Machine Learning of Reactive Potentials

Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction

Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection

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