Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 60 for “"Support vector regression"”.
-
Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes
Support vector machines (SVM) have been used to improve the ranking of single nucleotide polymorphisms (SNPs) over traditional chi-square tests in disease case studies [2]. In this investigation, ranking SNPs with support vector regression (SVR) was compared to the Wald test in predicting …
-
Prediction of arrival times of freight traffic on us railroads using support vector regression
… The ETA problem is posed as a machine learning regression problem and solved using a support vector regression machine trained and cross validated on over two years of historical data for a 140 mile stretch of track located primarily in Tennessee, USA. The article presents the data used in this …
-
Cost Modeling Based on Support Vector Regression for Complex Products During the Early Design Phases
… Tabu-SVR, a nonparametric approach based on support vector regression (SVR) for cost estimation for complex products in the early design phases. Tabu-SVR determines the parameters of SVR via a tabu search algorithm improved by the author. For verification and validation of performance on …
-
Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression
… genome SNPs data prediction methods built using Support Vector Regression (SVR) and Pearson Correlation Coefficient (PCC) to perform SNPs selection and then predict unobserved phenotype using ridge regression and SVR. The investigation shows that ranking SNPs by SVR significantly increases …
-
Surface Based Decoding of Fusiform Face Area Reveals Relationship Between SNR and Accuracy in Support Vector Regression
… FFA and used as training and testing labels in support vector regression (SVR) models. Both resting state and task data decoded activity in right FFA above chance, both within and between run types. Our method is not specific to resting state, potentially broadening the scope of research …
-
Diameter Estimation of Eucalyptus spp. Plantations in Southern Brazil Using Global Ecosystem Dynamics Investigation Data and Support Vector Regression
… to create a model of plantation diameter using Support Vector Regression (SVR). SVR enabled a robust model of tree diameter even given the heteroskedasticity and spatial auto correlation present in the GEDI data, which deleteriously impacted attempts at linear modeling. We could predict tree …
-
Privileged Machine Learning for Prediction
… learning, 4) slack variable learning in support vector regression.
-
Predicting price volatility crytocurrency ethereum
… to compute the forecast for the next two days: support vector regression and recurrent neural network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model …
-
Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates
… Integrated Moving Average (ARIMA) and Support vector regression (SVR) when predicting forex rates of US Dollar (USD) pair with South African Rand (ZAR) using daily timeframe data obtained from the Metatrader trading platform. The LSTM outperformed the SVR and ARIMA models according to …
-
Intra-field Nitrogen Estimation for Wheat and Corn using Unmanned Aerial Vehicle-based and Satellite Multispectral Imagery, Plant Biophysical Variables, Field Properties, and Machine Learning Methods
… Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models …
-
Application of Neural Network Techniques to Downscale Precipitation
… Artificial Neural Networks (ANN) and Hybrid Support Vector Regression (HSVR) methods to predict precipitation at a finer grid, based on the data from a coarse grid. Precipitation data for three stations (Dhaka, Comilla and Mymesnsingh in Bangladesh) was utilized. For each model, the raw data …
-
Machine Learning towards General Medical Image Segmentation
… We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object's contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and …
-
Design and development of advanced machine learning algorithms for lithium-ion battery state-of-charge estimation
… based on kNearest neighbor and random forest regression and a comparison study is done using four algorithms Support Vector Regression, Neural Network Regression, Random Forest Regression and kNearest Neighbor. Their performance is evaluated using data from two drive cycles.
-
A GAN-Augmented Machine Learning Framework for Predicting Raman Characteristics in Carbon Nanofiber Synthesis
… unaugmented baselines (XGBoost 𝑅2 ≃ 0.76; support vector regression 𝑅2 ≃ 0.71). CTGAN preserved data characteristics (≃ 96–97 % marginal fidelity / relationship), allowing robust generalization despite the small-n regime.
-
Predicción de la Actividad Económica Sectorizada a Corto Plazo en República Dominicana (2010–2025) mediante Técnicas Econométricas y de Machine Learning
… Para ello, se emplearon modelos AutoARIMA, Support Vector Regression, Random Forest (básico y optimizado), y redes neuronales (CNN y RNN), entrenados con más de 150 series económicas transformadas, y evaluados mediante validación cruzada temporal y métricas de desempeño relativo fuera de la …
-
Machine learning at the operating room of the future : a comparison of machine learning techniques applied to operating room scheduling
… take advantage of this richer data set: linear regression, nearest neighbors, regression trees, and support vector regression. We conclude that additional variables can improve the accuracy estimate by as much as 20%. Finally, we discuss the implementation challenges and future work necessary to …
-
Machine learning for nuclear fission systems : preliminary investigation of an autonomous control system for the MGEP
… Convolutional Neural Networks (CNNs) outperform Support Vector Regression (SVR) in predicting the MITR power-shape. Additionally, acceptable results were achieved when applying the CNN algorithm to the MGEP to predict the flux distribution of its fuel elements. Finally, it was verified that …
-
Data-driven Process-Structure-Property Models for Additive Manufactured Ni-base Superalloys
… PSP models. Both parametric and non-parametric regression techniques are employed to construct models to illustrate the suitability of the different ML methods. From well-established regression techniques, non-parametric support vector regression (SVR), and Gaussian-based modeling approaches …
-
Continuous dimensional emotion tracking in music
… in Music can be used when building feature vectors for machine learning solutions to the problem. I suggest some novel feature vector representation techniques, testing them on several datasets and several machine learning models, showing the advantage they can bring. Some of the suggested …
-
Mathematical Models of Biofilm in Various Environments
… thesis, we analyzed the biofilm activity with support vector regression. The machine learning model we obtained can be used to find the growth trends of biofilm for any pair of temperature and relative humidity data.
Page 1 of 3