Determining the number of training points required for machine learning of potential energy surfaces.

QR Code

Determining the number of training points required for machine learning of potential energy surfaces.

In recent years, there has been an explosion in the use of machine learning, with applications across many fields. One application of interest to the computational chemistry field is the use of a method known as Gaussian processes to accurately derive a system's Potential Energy Surfaces (PES)...

Full description

Bibliographic Details
Main Author:	Pearson, Matt
Format:	Thesis (University of Nottingham only)
Language:	English
Published:	2021
Subjects:	machine learning support vector machines Gaussian processes
Online Access:	https://eprints.nottingham.ac.uk/65589/

Similar Items

Efficient Training and Implementation of Gaussian Process Potentials
by: Broad, Jack W.
Published: (2022)

Real-time unsupervised incremental support vector machine for oil and gas pipeline NDT system
by: Nik Zulkepeli, Nik Ahmad Akram
Published: (2016)

Improving machine vision crack die detection with multi-kernel learning SVM
by: Abdullah, Mohd Khairi John
Published: (2024)

An evolutionary strategy with machine learning for learning to rank in information retrieval
by: Ibrahim, Osman Ali Sadek, et al.
Published: (2018)

Massively-parallel and concurrent SVM architectures
by: Phear, P. B. A.
Published: (2018)

A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
by: Mohd Pozi, Muhammad Syafiq
Published: (2016)

Application of machine learning for fuel consumption modelling of trucks
by: Perrotta, Federico, et al.
Published: (2017)

Using machine learning technique to classify geographic areas with socioeconomic potential for broadband investment in Malaysia
by: Tan, Taik Guan
Published: (2022)

Design and control of segmented triple three-phase SPM machines for fault tolerant drives
by: Sala, Giacomo, et al.
Published: (2017)

In-process measurement of the surface quality for a novel finishing process for polymer additive manufacturing
by: Syam, Wahyudin P., et al.
Published: (2018)

Development of a Neural-Fuzzy Model for Machinability Data Selection in Turning Process
by: Kong, Hong Shim
Published: (2008)

Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods
by: Jemwa, G., et al.
Published: (2012)

Improved feature extraction and lexicon reduction methods classified by support vector machine for Farsi handwritten word recognition system
by: Akbarpour, Shahin
Published: (2011)

Factors influencing the accuracy of remote sensing classifications: a comparative study
by: Pal, Mahesh
Published: (2002)

Machine learning techniques (support vector machine and artificial neural network) for fundamental and technical analysis: a study on SET and PSEI
by: Chin, Jern Tat
Published: (2021)

Age classification using Hierarchical Support Vector Machine based on characteristics of upper facial area
by: Dahlan, Hadi Affendy
Published: (2013)

Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process
by: Ali Al-Assadi, Hayder M. A.
Published: (2004)

Audiovisual classification of vocal outbursts in human conversation using long-short-term memory networks
by: Eyben, F., et al.
Published: (2011)

Online equation for latex application
by: Toh, Peck Yean
Published: (2017)

Detection on ambiguous software requirements specification written in malay using machine learning
by: Zahrin, Mohd Firdaus
Published: (2017)

Multilayer perceptrons neural network based web spam detection application
by: Goh, K.L., et al.
Published: (2013)

Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models
by: Pham, Hoa Thi, et al.
Published: (2022)

Forecasting Interest Rates from the Term Structure: Support Vector Machines Vs Neural Networks
by: Jacovides, Andreas
Published: (2008)

Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances
by: Tohira, Hideo, et al.
Published: (2021)

A support vector-based interval type-2 fuzzy system
by: Uslan, V., et al.
Published: (2014)

Radial force control for triple three-phase sectored SPM machines. Part I: Machine model
by: Sala, Giacomo, et al.
Published: (2017)

Monitoring of an industrial liquid–liquid extraction system with kernel-based methods
by: Jemwa, G., et al.
Published: (2005)

Poor transferability of machine learning classification models: classifying vegetation on palsa complexes in Arctic Sweden
by: Dancer, Holly
Published: (2024)

Direct flux and current vector control for induction motor drives using model predictive control theory
by: Odhano, Shafiq, et al.
Published: (2017)

Improving specific class mapping from remotely sensed data by cost-sensitive learning
by: Silva, Joel, et al.
Published: (2017)

Mathematical Information Retrieval (MIR) from scanned PDF and MathML conversion
by: Nazemi, Azadeh, et al.
Published: (2014)

Features selection for intrusion detection system using hybridize PSO-SVM
by: Tabaan, Alaa Abdulrahman
Published: (2016)

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates
by: Roadknight, Chris, et al.
Published: (2015)

Machine learning classification of macronutrient levels in oil palm trees with Landsat-8 imagery
by: Kok, Zhi Hong
Published: (2021)

Space vectors and pseudo inverse matrix methods for the radial force control in bearingless multi-sector permanent magnet machines
by: Sala, Giacomo, et al.
Published: (2018)

Figurative language detection using deep and contextual features
by: Razali, Md Saifullah
Published: (2023)

Towards uncertainty-aware and label-efficient machine learning of human expressive behaviour
by: Tellamekala, Mani Kumar
Published: (2022)

Cumulative attributes for pain intensity estimation
by: Joy, Egede, et al.
Published: (2017)

Designing and fabricating a twin polishing machine / Nik Azizul Hisham Nik Abd Aziz and Nik Ahmad Sallehuddin Nik Mohammed
by: Nik Abd Aziz, Nik Azizul Hisham, et al.
Published: (1992)

A fault detection method for railway point systems
by: Vileiniskis, Marius, et al.
Published: (2016)