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.
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Showing 1 to 20 of 171 for “"Support Vector Machine (SVM)"”.
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Using Real-Time Physiological and Behavioral Data to Predict Students' Engagement during Problem Solving: A Machine Learning Approach
… difficulty of each problem. Results from a Support Vector Machine (SVM) training indicated that problem outcomes could be correctly predicted from the combination of attention and workload signals at rates better than chance. The EEG data was also correlated with students' self-report of …
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Distributed Support Vector Machine With Graphics Processing Units
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it …
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SVM-based Harris Corner Detection to Classify Normal/Abnormal Breast Mammogram Images
… of the Computer-Aided Diagnosis (CAD) system to support diagnosis of breast cancer. In the CAD system, intensity value is a widely used feature for medical image processing. Classification of the breast mammogram image as normal or abnormal class is important, since it supports the early …
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Design of dust-filtering algorithms for LiDAR sensors in off-road vehicles using the AI and non-AI methods
… classifiers, such as Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). Two dust LiDAR datasets were collected and labeled for evaluation purposes. All proposed algorithms were implemented in the Robotic Operating System, allowing for the testing of these …
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External Support Vector Machine Clustering
The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data …
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Improving classification performance of cancer microarray data using hybridization of binary grey wolf and particle swarm optimization
… classifier such as K-nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF). Furthermore, we have used ratio comparison validation for the 10-folds cross-validation method for feature selection methods. Data sets such …
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Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data
… attempts to fill this need by investigating six machine learning methods to classify a dataset collected using a chemical sensor array: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and …
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Distributionally robust binary classifier under Wasserstein distance
… for the general problem. When focusing on the support vector machine (SVM), the general problem boils down to an easy-to-solve second- order cone programming problem. The robustified SVM is then applied to synthetic data with and without contamination, and our simulation studies show that our …
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Automatic Software Performance Optimization on Modern Architectures
… input data. In the dissertation, we present a machine learning based approach to select the best frequent pattern mining algorithm based on the input characteristics. Three of the fastest publicly available algorithms, FP_Growth, LCM and Eclat, were extensively evaluated using synthetic data …
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Towards trainable man-machine interfaces : combining top-down constraints with bottom-up learning in facial analysis
… proposes a miethodology for the design of man-machine interfaces by combining top-down and bottom-up processes in vision. From a computational perspective, we propose that the scientific-cognitive question of combining top-down and bottom-up knowledge is similar to the engineering question of …
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An Artificial Intelligence Based Approach to Automate Document Processing in Business Area
… With Optical Character Recognition (OCR) and machine learning techniques, businesses are able to apply Artificial Intelligence (AI) to automate the process. However, introducing an AI application to business is challenging; it is easy to fail because of the complexity between the technical and …
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Fusion Approaches to Individual Tree Species Classification Using Multi-Source Remotely Sensed Data
… investigated fusion approaches deployed with Support Vector Machine (SVM) and Random Forest (RF) algorithms to incorporating multispectral imagery (MSI), a very high spatial resolution panchromatic image (PAN), and Light Detection and Ranging (LiDAR) data for five object-based tree species …
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Recognition of Arabic handwritten words
… we use holistic approach to recognize PAW using Support Vector Machine (SVM) and Active Shape Models (ASM). While there are few works that use SVM to recognize PAW, they use a small dataset; we use a large dataset and a different set of features. We also explain the errors SVM and ASM make and …
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The Multimodal Interaction through the Design of Data Glove
… system is to help users to interact with the machine naturally by recognizing various gestures from the user from a wearable device. To achieve this goal, we have implemented a system including both hardware solution and gesture recognizing approaches. For the hardware solution, we designed …
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Risk prediction with genomic data
… study (GWAS) is widely used with various machine learning algorithms to predict disease risk. This thesis investigates this widely used approach of GWAS using Single Nucleotide Polymorphism (SNP) genotype data and a novel approach of disease risk prediction with whole exome sequencing …
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Applying Reservoir Computing for Driver Behavior Analysis and Traffic Flow Prediction in Intelligent Transportation Systems
… providing real-world driving dynamics. Through a support vector machine (SVM) algorithm, we categorize drivers based on their performance, offering insights for tailored anomaly detection strategies. This research advances anomaly detection for autonomous vehicles, promoting safer driving …
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Métodos Machine learning aplicados para estimar la concentración de los contaminantes de la DQO y de los SST en hidrosistemas de saneamiento urbano a partir de espectrometría UV-Visible
… nuevas metodologías basadas en métodos machine learning, para lo cual se implementaron tres técnicas de inteligencia artificial denominadas: Support Vector Machine (SVM), Redes Neuronales Artificiales (RNA) y algoritmos evolutivos. Éste último fue empleado para realizar una optimización …
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Maritime Object Detection, Tracking, and Classification Using Lidar and Vision-Based Sensor Fusion
… Objects are then extracted and classified with a Support Vector Machine (SVM) classifier. The LiDAR returns, when converted from a global frame to a camera frame, then allow the cameras to process a region of their imaging frame to assist in the classification of objects using color-based …
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Depth sensor based object detection using surface curvature
… curvature and mean curvature. The linear Support Vector Machine (SVM) is employed for the object detection task in this work. We evaluate our proposed HOC feature on two widely used datasets and compare the results with other well-known object detection methods applied on both RGB images …
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Improved prediction of gene expression of epigenomics data of lung cancer using machine learning and deep learning models
… among various diseases including cancers. Machine learning is frequently used in cancer diagnosis and detection. In this research, four types of data are used towards the correct prediction of lung cancer, including DNA Methylation data, Histone data, Human Genome data, and RNA-Seq data. …
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