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 782 for “"Classifiers"”.
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The Kiriwinan classifiers
The Kiriwina language is spoken by a population of 16,000 people on the Northern fringe of the Milne Bay Province in Papua New Guinea. The majority of this number live in the Trobriand Islands Group, some 12,000 on the main island, Kiriwina, and 2,500 on other islands of the group. The rest of the …
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The Kiriwinan classifiers
The Kiriwina language is spoken by a population of 16,000 people on the Northern fringe of the Milne Bay Province in Papua New Guinea. The majority of this number live in the Trobriand Islands Group, some 12,000 on the main island, Kiriwina, and 2,500 on other islands of the group. The rest of the …
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Multiparty Protocols for Tree Classifiers
… OPE. Decision trees are common and very popular classifiers because they are explainable. The problem of evaluating a private decision tree on private data consists of a server holding a private decision tree and a client holding a private attribute vector. The goal is to classify the client’s …
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Towards Deployable Robust Text Classifiers
… task in natural language processing. Deploying classifiers enables more efficient information processing, which is useful for various applications, including decision-making. However, classifiers also present challenging and long-standing problems. As their use increases, expectations about …
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Learning classifiers from medical data
… to use machine-learning techniques to discover classifiers from a database of medical data. Through the use of two software programs, C5.0 and SVMLight, we analyzed a database of 150 patients who had been operated on by Dr. David Rattner of the Massachusetts General Hospital. C5.0 is an …
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Enhanced knowledge distillation by auxiliary classifiers
… backbone teacher is frozen. The cohort of all classifiers supervises the student in the last step. EKD showed superior performance in comparison with some of the state-of-the-art distillation frameworks.
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Aspects of generative and discriminative classifiers
… new terminology of generative and discriminative classifiers, research interest in classical statistical approaches to discriminant analysis has re-emerged in the machine learning community. In discriminant analysis, observations with features $\mathbf{x}$ measured are classified into classes …
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Incremental random forest classifiers in spark
The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents …
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Machine learning classifiers for critical cardiac conditions
… this thesis, novel features and machine learning classifiers are developed for automating the detection of Congestive Heart Failure (CHF) and Atrial Fibrillation (AFIB). These classifiers can potentially trigger alarms when implemented in wearable devices. In the first experiment, quantitative …
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Titanium bead calibration of deep net classifiers
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Context-aware activity recognition using TAN classifiers
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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Classifiers : adaptive modules in pattern recognition systems
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.
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Data Attribution: From Classifiers to Generative Models
… and scales in the supervised setting: image classifiers trained on ImageNet, vision-language models (CLIP), and language models (BERT and mT5). Then, we extend TRAK to the generative setting, and show that it can be used to attribute different classes of diffusion models (DDPMs and LDMs).
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Hardening DGA Classifiers Using Adversarial Attacks and IVAP
… importance. A multitude of machine learning classifiers have been developed for the detection of DGAs, which are intended to be able to detect a specific DGA after training on domains generated by it. CharBot is an incredibly simple DGA that has been demonstrated to be very effective at …
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Comparison and performance enhancement of modern pattern classifiers
… differences. We take Gaussian process classifiers as an example, which shows disappointing number of performance evaluations in the literature. What is particularly ignored is any use of the uncertainties in the performance measures when making comparisons. This thesis makes a novel …
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Decision tree classifiers for incident call data sets
Information technology (IT) has become one of the key technologies for economic and social development in any organization. Therefore the management of Information technology incidents, and particularly in the area of resolving the problem very fast, is of concern to Information technology …
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Adversarial training objectives for generative attacks on text classifiers
… challenging. These examples aim to deceive text classifiers into incorrect predictions while maintaining linguistic similarity to genuine inputs. This complexity arises from the need to preserve the original text's fluency, grammaticality, and semantic coherence, which complicates the generation …
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A Machine Learning Classifiers Approach for Cardiovascular Disease Diagnosis
… in cardiology and the role that ensemble classifiers can play to help diagnose cardiovascular disease. The computing power and technology available to humans has helped in the development of the application of computers in cardiology. With this development comes a redundancy of some data. …
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Efficient object detection via structured learning and local classifiers
… two locally linear and one locally nonlinear classifiers to approximate the nonlinear decision boundaries in the feature space efficiently. Inspired by the kernel trick, these classifiers map the original features into another feature space explicitly where linear classifiers are employed for …
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