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Results for multiple kernel function

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14 papers · ranked by Valyu relevance

01
AarXiv2021
Neural Generalization of Multiple Kernel Learning
Ahmad Navid Ghanizadeh, Kamaledin Ghiasi-Shirazi, Reza Monsefi, Mohammadreza Qaraei
'Mohammadreza Qaraei'] Multiple Kernel Learning is a conventional way to learn the kernel function in kernel-based methods. MKL algorithms enhance the performance of kernel methods. However, these methods have a lower complexity compared to deep learning models and are inferior to these models in terms of recognition…
83relevance
02
AarXiv26 Mar 2024
MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation
Weiguo Gao
Length Extrapolation Authors: ['Weiguo Gao'] When the predicted sequence length exceeds the length seen during training, the transformer's inference accuracy diminishes. To address these challenges, the ability to generalize or extrapolate to longer sequence lengths has garnered increased attention. Existing relative…
81relevance
03
AarXiv8 Jul 2017
Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples
Nora K. Speicher, Nico Pfeifer
Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene…
80relevance
04
AarXiv28 Nov 2018
Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating
Nicolas Langrené, Xavier Warin
Kernel density estimation and kernel regression are powerful but computationally expensive techniques: a direct evaluation of kernel density estimates at M evaluation points given N input sample points requires a quadratic O(MN) operations, which is prohibitive for large scale problems. For this reason, approximate…
77relevance
05
AarXiv2012
Multiple Operator-valued Kernel Learning
Hachem Kadri, Alain Rakotomamonjy, Francis Bach, Pierre‐Marie Preux
Positive definite operator-valued kernels generalize the well-known notion of reproducing kernels, and are naturally adapted to multi-output learning situations. This paper addresses the problem of learning a finite linear combination of infinite-dimensional operator-valued kernels which are suitable for extending…
75relevance
06
AarXiv2015
Operator-valued Kernels for Learning from Functional Response Data
Hachem Kadri, Emmanuel Duflos, Pierre‐Marie Preux, Stéphane Canu + 2 more
'Alain Rakotomamonjy' 'Julien Audiffren'] In this paper1 we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel Hilbert space…
72relevance
07
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…
64relevance
08
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…
63relevance
09
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…
61relevance
10
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
11
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
59relevance
12
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…
59relevance
13
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…
58relevance
14
CchemRxiv5 Apr 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…
55relevance

Results for multiple kernel function

Neural Generalization of Multiple Kernel Learning

MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation

Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples

Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating

Multiple Operator-valued Kernel Learning

Operator-valued Kernels for Learning from Functional Response Data

Accurate and Robust Analysis of Molecular Kinetics with Random Features

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

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

Machine Learning of Reactive Potentials

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

Krein Support Vector Machine Classification of Antimicrobial Peptides

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

Neural Generalization of Multiple Kernel Learning

MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation

Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples

Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating

Multiple Operator-valued Kernel Learning

Operator-valued Kernels for Learning from Functional Response Data

Accurate and Robust Analysis of Molecular Kinetics with Random Features

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

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

Machine Learning of Reactive Potentials

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

Krein Support Vector Machine Classification of Antimicrobial Peptides

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