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 42 for “"SVM classifier"”.
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Support Vector Machines (SVMs) Based Framework for Classification of Fallers and Non-Fallers
… techniques, e.g. support vector machines (SVMs). Additionally, as the assessment of fall risk is linked to noisy environment, it is important to understand the capability of the SVM classifier to effectively address noisy data. Therefore, the robustness of the SVM classifier was also …
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NEW ADVANCES IN QUANTITATIVE RADIOLOGY: RADIOMICS IN NEURORADIOLOGY APPLIED TO PRIMARY BRAIN TUMORS USING A MACHINE LEARNING APPROACH
… features for IDH prediction. For IDH prediction, SVM classifier achieved average 96.8% accuracy and 0.929 AUC during the training phase, and 84.6% accuracy and 0.60 AUC on the test set. For MGMT methylation prediction, SVM classifier achieved average 67.7% accuracy and 0.765 AUC during the …
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The performance of soft computing techniques on content-based SMS spam filtering
… Neural Network and Support Vector Machines (SVM) are for content-based SMS spam filtering using an appropriate size of features which are selected by the Gini Index metric as it has the ability to extract suitable features from imbalanced data sets. The data sets used in this research were …
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Efficient homomorphically encrypted privacy-preserving automated biometric classification
… a homomorphically encrypted implementation of a SVM classifier. We provide experimental demonstrations of the accuracy and practical efficiency of both of these algorithms.
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Comparing Naïve Bayes Classifiers with Support Vector Machines for Predicting Protein Subcellular Location Using Text Features
… papers), and apply a support vector machine (SVM) classifier to classify proteins into their respective locations. Both EpiLoc and HomoLoc’s prediction accuracy is comparable to that of state-of-the-art protein location prediction systems. However, in addition to accuracy, other factors such …
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The comparative study of model-based and appearance based gait recognition for leave bag behind
… Academy of Science (CASIA) and tested using two classifiers which is Support Vector Machine (SVM) and KNN (K nearest Neighbour) based on accuracy and misclassification rates (MER) metrics. The experiment results show that the accuracy and misclassification rate (MER) of Appearance-based …
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Clustering Via Supervised Support Vector Machines
An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM …
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Utilizing network features to detect erroneous inputs
… a single model. Specifically, I train a linear SVM classifier to detect these four types of erroneous data using the hidden and softmax feature vectors of pre-trained neural networks. Results indicate that these faulty data types generally exhibit linearly separable activation properties from …
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Computer aided analysis of skin lesions
… The optimal cluster centers act as input to the SVM classifier and the kernel parameters are obtained. Finally, parameters of the kernel function are optimized by genetic algorithm, which help in classifying the skin lesions into various grades leading to early diagnosis of skin cancer.
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A de-identifier for electronic medical records based on a heterogeneous feature set
… and de-identifiers and using them in a SVM classifier. We show that the benefit from having an inclusive set of features outweighs the harm from the very large dimensionality of the resulting classification problem. We also show that our classifier does not over-fit the training data. We …
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Maritime Object Detection, Tracking, and Classification Using Lidar and Vision-Based Sensor Fusion
… 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 features. The SVM implementation …
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Neural network architectures for Prepositional Phrase attachment disambiguation
… offer a learning-to-rank algorithm based on an SVM classifier which has access to a wide range of features. The performance of this system is compared to the compositional models.
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Rekurentinių diagramų tipo metodai elektroencefalogramų analizei /
… result was achieved with EEG fragments of 16 s. SVM classifier showed an accuracy of 72\%, neural network - 67\%. Doctors distinguish two types of epilepsy by analyzing epileptiform discharges also known as spikes. In this work, the author tried to classify the epilepsy types by performing the …
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Automated Detection of Maternal Vascular Malperfusion Lesions in Human Placentas Diagnosed with Preeclampsia and Fetal Growth Restriction Using Machine Learning
… used to develop various support vector machine (SVM) classifier models, differing in feature extraction methods. Classification performance of each model was assessed through accuracy, precision, and recall using confusion matrices. Results: SVM models demonstrated accuracies between 47-73% in …
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Pattern recognition analysis on heavy ion reaction data
… events. To this end, a support vec- tor machine (SVM) classifier is adopted to analyze multifragmentation reactions. This method allows to backtracing the values of b through a particular multidimensional analysis. The SVM classification con- sists of two main phase. In the first one, known as …
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Thin slices of interest
… most important role, and a simple activity-based classifier predicts low or high interest with 74% accuracy (for men). In the speed-dating study, we use the speech features measured from five minutes of conversation to predict attraction between people. The features predict 40% of the variance in …
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Context Aware Textual Entailment
… and objects. The method includes a two-phase SVM classifier along with a voting mechanism in the second phase to identify the contexts. Rule-based algorithms were utilized to extract the context elements. This research also develops a new context˗aware text representation. This representation …
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Machine Learning Classification of Traumatic Brain Injury Patients Versus Healthy Controls Using Arterial Spin Labeled Perfusion MRI
… learning, specifically a Support Vector Machine (SVM) classifier, in discriminating between healthy controls (n=35) and TBI patients (n=42) using ASL-generated CBF data 3 months post-injury. Identification of the regions of interest (ROIs) most predictive of TBI is also explored as part of this …
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Support Vector Machine algorithms : analysis and applications
Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs …
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