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 168 for “"Multi-class"”.
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Multi-Class Classification in Natural Language Processing
… this thesis as an extension of the current classification methods which aim at disambiguating among many classes.
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Beyond multi-class – structured learning for machine translation
… sub-problem encountered in the MT field as a classification or regression problem. To model specific mappings in MT tasks, the modern machine learning paradigm known as “structured learning” is pursued. This approach goes beyond classic multiclass pattern classification and explicitly models …
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Improving multi-class text classification with Naive Bayes
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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QoS Provisioning for Multi-Class Traffic in Wireless Networks
… provisioning for wireless networks that carry multiple classes of traffic a complex problem. We have developed a set of admission control and resource reservation schemes for QoS provisioning in multi-class wireless networks.</p> <p>We present three variations of a novel resource borrowing …
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Investigating Ensembles of Single-class Classifiers for Multi-class Classification
Traditional methods of multi-class classification in machine learning involve the use of a monolithic feature extractor and classifier head trained on data from all of the classes at once. These architectures (especially the classifier head) are dependent on the number and types of classes, and are …
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Multi-class segmentation of brain tumor using Convolution Neural Network
… Network (CNN) architecture is used to segment multi-modal Brain Tumors from Magnetic Resonance (MR) images. Due to the challenges in manual segmentation, computerized brain tumor segmentation is one of the most important challenges in medical imaging. The fully convolutional structure of the …
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Multi-label/multi-class Deep Learning Classification Of Spatiotemporal Data
… activity in machine learning requires the use of multi-labels. In order to detect concurrent occurrences spatially, the labels should represent the regions of interest for a particular application. For example, in this thesis, the regions of interest will be either different quadrants of a parking …
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Multi-Label/Multi-Class Deep Learning Classification of Spatiotemporal Data
… activity in machine learning requires the use of multi-labels. In order to detect concurrent occurrences spatially, the labels should represent the regions of interest for a particular application. For example, in this thesis, the regions of interest will be either different quadrants of a parking …
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Unsupervised anomaly detection in multi-class datasets using Generative Adversarial Networks
… a dataset during training. To achieve this, a multi-generator network is first implemented, where each generator is responsible for learning a unique manifold of data. Second, a machine learning mechanism called a ''bandit"" is implemented to find the optimal set of generators required to cover …
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Large-scale Optimization for Robust Multi-Class Prediction and Resource Allocation
… from data, first in the context of robust multi-class prediction and second for prescriptive analytics for medical resource allocation. In the first part, we make progress on training robust multi-class classifiers using error-correcting output codes (ECOC). We propose linear and non-linear …
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Joint Gaussian Graphical Model for multi-class and multi-level data
… for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are …
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Unpacking the role of complexity in multi-class models of the tumor microenvironment
… are a popular approach to study emergence in multi-scale systems with complex interactions. ABM modularity provides a means of incorporating mutiple classes of models that can be regulated at different scales in an intuitive manner. However, robust analysis methods remain an open challenge. …
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Multi-Class 3D Segmentation of Progressive Damage in Advanced Composites using Deep Learning
… of fiber break damage mechanisms, (ii) Multi-damage (fiber break and matrix crack) segmentation, and (iii) 3D multi-class segmentation of a benchmark sandstone dataset. Multi-class segmentation of microscale damage is proposed to automate CT segmentation in the advanced composite, …
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Self-training for cyberbully detection: Achieving high accuracy with a balanced multi-class dataset
Cyberbullying has become an alarming issue in the digital era, causing significant harm to its victims. The development of automated methods for detecting cyberbullying in social media is of paramount importance to safeguard vulnerable individuals. In this thesis, we propose a robust approach based …
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An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis
This thesis investigated a multi-class auditory P300 BCI as a step towards FES applicability. A multi-class P300 paradigm approach provides degrees-of-freedom in operating an FES device over the traditional P300 paradigm. Accuracy in classification of target P300s contributes to the paradigm's …
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Towards a framework for multi class statistical modelling of shape, intensity and kinematics in medical images
… a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for …
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Towards a framework for multi class statistical modelling of shape, intensity, and kinematics in medical images
… a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for …
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ESTIMATING PARAMETERS OF A MULTI-CLASS IZHIKEVICH NEURON MODEL TO INVESTIGATE THE MECHANISMS OF DEEP BRAIN STIMULATION
… model was used to accomplish this task and four classes of neurons were modeled. The parameters of each class were estimated using a genetic algorithm with a fitness function based on spike frequency as a function of input current. After computing the optimal parameters the neurons were …
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