Essays about: "kernel ridge regression"
Showing result 1 - 5 of 10 essays containing the words kernel ridge regression.
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1. Kernel Methods for Regression
University essay from Linnéuniversitetet/Institutionen för matematik (MA)Abstract : Kernel methods are a well-studied approach for addressing regression problems by implicitly mapping input variables into possibly infinite-dimensional feature spaces, particularly in cases where standard linear regression fails to capture non-linear relationships in data. Therefore, the choice between standard linear regression and kernel regression can be seen as a tradeoff between constraints on the number of features and the number of training samples. READ MORE
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2. HRTF-Based Sound Field Interpolation in the Presence of a Human Head
University essay from Lunds universitet/Matematisk statistikAbstract : There are different ways of evaluating the sound pressure in a sound field. Doing so is an essential part of several applications, such as active sound control and voice analysis. READ MORE
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3. Predicting Reactor Instability Using Neural Networks
University essay from KTH/FysikAbstract : The study of the instabilities in boiling water reactors is of significant importance to the safety withwhich they can be operated, as they can cause damage to the reactor posing risks to both equipmentand personnel. The instabilities that concern this paper are progressive growths in the oscillatingpower of boiling-water reactors. READ MORE
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4. Modelling Magnetism of hcp Iron under Earth’s Inner Core Conditions : Based on first-principle DFT calculations and Machine Learning
University essay from Linköpings universitet/Teoretisk FysikAbstract : The structure of Earth’s core remains largely a mystery. The solid inner core is believed to exist in extreme pressure and temperature conditions comparable to 300 GPa and 6000 K and consists mainly of iron, Fe. READ MORE
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5. Accelerating bulk material property prediction using machine learning potentials for molecular dynamics : predicting physical properties of bulk Aluminium and Silicon
University essay from Linköpings universitet/Teoretisk FysikAbstract : In this project machine learning (ML) interatomic potentials are trained and used in molecular dynamics (MD) simulations to predict the physical properties of total energy, mean squared displacement (MSD) and specific heat capacity for systems of bulk Aluminium and Silicon. The interatomic potentials investigated are potentials trained using the ML models kernel ridge regression (KRR) and moment tensor potentials (MTPs). READ MORE
