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 12 of 12 for “"discriminative models"”.
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Learning discriminative models with incomplete data
… to incorrect labels in the training data. The discriminative paradigm of classification aims to model the classification boundary directly by conditioning on the data points; however, discriminative models cannot easily handle incompleteness since the distribution of the observations is never …
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Generative and Discriminative Models in Phase Transition Prediction
… understanding physical systems. Generative and discriminative models offer promising yet distinct approaches. Considering varying knowledge levels of the system, accessible data amounts, and computation resources of the experiments, these methods exhibit different accuracy and efficiency. This …
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Generative and discriminative models for person verification and efficient search
Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-10T20:41:56Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Li_Zhen.pdf: 10718613 bytes, checksum: 33d3a5f6a45d0870d945906fb9961656 (MD5)
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Improving breast cancer risk assessment with image-based deep learning models
Discriminative models for breast cancer risk prediction are needed in order to provide personalized patient care. Existing breast cancer risk models incorporate information about breast tissue using imaging biomarkers such as density scores. However, these imaging biomarkers are limited in that …
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EFFORTS TOWARD TRUSTWORTHY MACHINE LEARNING: MITIGATING OVERCONFIDENCE, HALLUCINATION, AND MODALITY BIAS
… high-stakes domains. Despite strong performance, models remain prone to reliability issues such as overconfidence, hallucinations, and modality bias. This thesis addresses these challenges through post-hoc methods and targeted fine-tuning strategies. First, we mitigate overconfidence in …
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Discrimination of alcoholics from non-alcoholics using supervised learning on resting EEG
… learning on resting scalp EEG data to build models that can match clinical diagnoses of alcohol dependence. We extract features from four minute eyes-closed resting scalp EEG recordings, and use these features to train discriminative models for identifying alcohol dependence. We found that we …
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Gene identification using phylogenetic metrics with conditional random fields
… has been addressed using graphical probabilistic models of genomic sequence. While such models have been successful for small genomes with relatively simple gene structure, new methods are necessary for scaling these to the complete human genome, and for leveraging information across multiple …
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Guiding Deep Probabilistic Models
Deep probabilistic models utilize deep neural networks to learn probability distributions in high-dimensional data spaces. Learning and inference in these models are complicated due to the difficulty of direct evaluation of the differences between the model distribution and the target. This thesis …
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Net-PPI : mapping the human interactome with machine learned models
… for training and evaluation of machine learning models; a comprehensive study of protein sequence representations for use with discriminative models; and data splitting methodology for machine learning purposes. We also present the Bilinear PPI model for state-of-the-art PPI prediction. Finally, …
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Improve the efficiency of conditional generative models
Deep generative models have undergone significant advancements, enabling the production of high-fidelity data across various fields, including computer vision and medical imaging. The availability of paired annotations facilitates a controllable generative process through conditional generative …
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Spoken Language Understanding: from Spoken Utterances to Semantic Structures
… proposed in the last decades: generative and discriminative models. The former are robust to over-fitting and they are less affected by noise but they cannot easily integrate complex structures (e.g. trees). In contrast, the latter can easily integrate very complex features that can capture …
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Layout-aware mixture models for patch-based image representation and analysis
… comprised of millions of pixels, developing models in such a high dimensional space is not always feasible. One of the most popular ways of modeling images is to break them into patches; the reason is that not only is the dimensionality reduced, but it is easier to define similarities between …