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 298 for “"domain knowledge"”.
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Domain knowledge acquisition via language grounding
… extraction. We propose methods to acquire knowledge represented in the form of relations and utilize them in two domain applications, high-level planning in a complex virtual world and input parser generation from input format specifications. In the first application, we propose a …
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Domain knowledge specification using fact schema
The advantages of integrating artificial intelligence (AI) Technology with data base management system (DBMS) technology are widely recognized as indicated by the results from the survey of AI and data base (DB) researchers. ...In our work, we have focused on the use of data base systems to store …
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ENHANCING DEEP LEARNING WITH SYMBOLIC DOMAIN KNOWLEDGE
… of training data. On the other hand, symbolic domain knowledge is often available in addition to data. The first part of this thesis aims to improve data efficiency by incorporating symbolic domain knowledge. We propose logic graph embedding frameworks, Logic Embedding Network with Semantic …
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Applying domain knowledge to clinical predictive models
… thesis, we explored two different ways to apply domain knowledge to improve clinical predictive models. We first applied knowledge about the heart to engineer better frequency-domain features from electrocardiograms (ECG). The standard frequency domain (in Hz) quantifies events that repeat with …
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Deep Learning for Unstructured Data by Leveraging Domain Knowledge
… address a fundamental problem in the text mining domain, i.e, embedding of rare and out-of-vocabulary (OOV) words, by refining word embedding models and character embedding models in an iterative way. We illustrate the simplicity but effectiveness of our method when applying it to online …
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The use of domain knowledge in optimal information aggregation
In this thesis, I present some novel results pertaining to the relationship between two popular and interesting information aggregation methods: the Condorcet and Borda tallies. I present numerical results showing how the much simpler Borda tally can be used to approximate the outcome of the …
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Improving human movement sensing with micro models and domain knowledge
… human sensing methods can be improved with domain knowledge. Specifically, we propose expert hierarchies (EHs) as an intuitive way to encode domain knowledge and simplify multi-class HAR, without negatively affecting predictive performance. The advantages of EHs are that they have lower time …
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The effects of biographical data on the prediction of domain knowledge
… life experience information on the prediction of domain knowledge. Specifically, it was hypothesized that individuals with a higher level of experience within a domain would have a higher level of domain knowledge, and that attribution of experience (e.g., educational experience, extracurricular …
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Improving Exploration in Reinforcement Learning through Domain Knowledge and Parameter Analysis
… exploration in reinforcement learning using domain knowledge and knowledge-based approaches to reinforcement learning. It also identifies novel relationships between the algorithms' and domains' parameters and the exploration efficiency. The goal of solving reinforcement learning problems is …
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Opportunistic constructive induction: Using fragments of domain knowledge to guide construction
One subfield of machine learning is the induction of a representation of a concept from positive and negative examples of the concept. Given a set of training examples, the goal of the inductive system is to create a description capable of classifying the training examples, yet general enough to …
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Investigating genetic programming with novelty and domain knowledge for program synthesis
… support of both programming and problem specfic knowledge. We attempt to transfer insights from such human expertise to genetic programming (GP) for solving automatic program synthesis. We draw upon manual and non-GP Artificial Intelligence methods to extract knowledge from synthesis problem …
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DOMAIN KNOWLEDGE-GUIDED LEARNING FOR ROBUST MYOCARDIAL INFARCTION DETECTION FROM 12-LEAD ELECTROCARDIOGRAMS
… models often overlook the electrocardiographic domain knowledge (DK) codified into clinical decision rules. Without the explicit incorporation of such DK, these models may learn feature representations that are not physiologically grounded, thereby reducing their clinical generalisability. …
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Intelligent Industrial Maintenance: Using Natural Language Processing Technology and Domain Knowledge for Decision Support
… particularly large language models (LLMs), and domain-specific knowledge to provide decision support on predictive maintenance, fault diagnosis, and maintenance task planning. The proposed framework consists of three key components: 1) a multi-modal fusion methodology for integrating sensor data …
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Explanation-Based Approach to Incorporating Domain Knowledge Into Support Vector Machine: Theory and Applications
We also present the comparison of the three proposed approaches, discuss about the related work, and point out some future work. We believe this work provide a first step towards a new research area in machine learning.
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Probabilistic modeling of the drug development domain: A Bayesian domain-knowledge application for pharmacovigilance
… thesis is concerned with developing a Bayesian domain-knowledge probabilistic model (called Pharminator) to address the first two of these categories, with a goal of predicting clinical success of an NCE. Pharmacoeconomic modeling is a vastly different domain compared to Pharminator's clinical …
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Towards NextG Receiver: Online Real-Time Machine Learning with Domain Knowledge for Wireless Communications
… estimation task. To enable efficient learning, domain knowledge, such as the symmetric structure of the modulation constellation, the delay-Doppler (DD) domain input-output relationship, and the channel statistics, is inherently embedded in the design of the neural network. All introduced …
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