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 63 for “"Support vector machines (SVM)"”.
-
Evaluation of different Support Vector Machines (SVM) for speaker identification
This study is an investigation into four support vector machines (SVM) kernels. SVMs have gained much acceptance in classification tasks since their inception in the 1990s. The central feature of SVM is the implicit mapping of input data to some higher-dimensional feature space. This is achieved …
-
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 …
-
Real Estate Valuation in Buenos Aires, an Interactive Tool Development
… including XGBoost, Random Forest, and Support Vector Machines (SVM), selected for their robustness and efficiency in handling large datasets. Our input data consists of 64,358 property listings from the e-commerce platform Mercado Libre, obtained through a combination of Python scripts …
-
Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture
… issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based …
-
Face authentication with pose adjustment using support vector machines with a Hausdorff-based kernel
… can be normalized to have the same pose. Using support vector machines (SVM) as the classifier, the second method uses a Hausdorff-based kernel embedded in the SVM decision function. The Hausdorff-based kernel has been shown to improve accuracy in object recognition. Using these two methods, the …
-
ESSAYS ON NUDGING CUSTOMERS’ BEHAVIORS: EVIDENCE FROM ONLINE GROCERY SHOPPING AND CROWDFUNDING
… the limited number of coupons. We develop a Support Vector Machines (SVM) based approach to rank order customers. We conduct a field experiment in an online grocery store to evaluate how well the identified customers are nudged through information and/or couponing. We find that, in terms of …
-
ANALYZING AND PREDICTING ARMY COMBAT FITNESS TEST PERFORMANCE: A STATISTICAL AND MACHINE LEARNING APPROACH
… techniques—including Logistic Regression (LR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Random Forests (RF), and Artificial Neural Networks (ANN)—to predict ACFT outcomes using raw ACFT scores alongside demographic and body composition …
-
Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data
… is most commonly used. Many approaches such as Support Vector Machines (SVM), Basic Neural Networks (BNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN) have been highly used in wildfire prediction. The goal of this research is …
-
Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models
… preprocessing, the dataset was analyzed using Support Vector Machines (SVM), VGG-19, and YOLOv10 models. Metrics including accuracy, precision, recall, F1-score, and ROC-AUC were utilized to evaluate the model's effectiveness. The findings reveal that hybrid models, particularly SVM+VGG-19, …
-
Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks
… the performance of deep networks relative to support vector machines (SVM). Using the methylation states of sequentially neighboring regions as one of the learning features, SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially …
-
Algorithm to enable intelligent rail break detection
… has been furthered and paired with an SVM based classifier. The wavelet intensity algorithm acts as a feature extraction algorithm. The wavelet transform is an effective tool as it allows one to narrow down upon the transient, high frequency events and is able to tell their exact …
-
Supervised Machine Learning Under Test-Time Resource Constraints: A Trade-off Between Accuracy and Cost
… of transforming data instances to feature vectors, and could be highly variable when features are heterogeneous. The latter reflects the effort of evaluating a classifier, which could be substantial, in particular nonparametric algorithms. We then propose three strategies to explicitly …
-
A Machine Learning Based Victim's Scream Detection System for Burning Sites Using an Autonomous Embedded System Vehicle
… of three machine learning (ML) approaches: Support Vector Machines (SVM), Long Short-Term Memory (LSTM) and transfer learning with Yet Another Mobile Network (YAMNet). The performance of these three techniques has been evaluated based on a variety of performance metrics. The models with top …
-
Use of Machine Learning for Automated Convergence of Numerical Iterative Schemes
… that may be inherent to the solution. Using a Support Vector Machines (SVM) machine learning approach, an algorithm is designed to use the source data to train a model to predict convergence in the solution process and stop unnecessary iterations. The discretization of the Navier Stokes (NS) …
-
Classification of Human Postural and Gestural Movements Using Center of Pressure Parameters Derived From Force Platforms
… such as nearest neighbor classifiers, support vector machines (SVM), and neural networks were explored and successfully applied to the aforementioned movement classification. The average classification rates on test sets ranged from approximately 79% to 92%. All the methods proposed in …
-
Quantitative Structure-Activity Relationship Modeling to Predict Drug-Drug Interactions Between Acetaminophen and Ingredients in Energy Drinks
… descriptors were calculated. The multi-label support vector machines (SVM) method was used for classification and the K-means method was used to cluster the data. The model was validated in vitro by exposing Hepa1-6 cells to select compounds found in energy drinks and assessing cell death. …
-
Evaluating Neuroimaging Modalities in the A/T/N Framework: Single and Combined FDG-PET and T1-Weighted MRI for Alzheimer’s Diagnosis
… Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in combination, within the AD continuum. Our analysis, under the A/T/N framework's 'N' category, reveals that FDG-PET consistently …
-
Comparison of Random Forests, Support Vector Machine and Artificial Neural Network Methods for Agriculture Land Cover Classification
… techniques, such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can be applied in land cover classification. However, putting a machine learning categorization system in place is not easy, especially in the field of agricultural land …
-
Machine-Learning-Based Non-Destructive Evaluation of Refractory Anchor Welds via Analysis of Percussion-Induced Acoustic Signals
… models. The supervised models used were support vector machines (SVM), logistic regression, recurrent neural networks (RNN). The unsupervised models used were k-means clustering. All models were evaluated using three progressively independent tests: a dependent 70:30 train-test split, a …
-
Machine Learning Classification of Gas Chromatography Data
… is a field consisting of techniques by which machines can independently analyze data to derive their own procedures for processing it. Additionally, there are techniques for enhancing the performance of ML algorithms. Feature Selection is a technique for improving performance by using a …
Page 1 of 4