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 615 for “"Predictive Models"”.
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Active evaluation of predictive models
… machine learning studies algorithms that infer predictive models from data. Predictive models are applicable for many practical tasks such as spam filtering, face and handwritten digit recognition, and personalized product recommendation. In general, they are used to predict a target label for a …
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Applying domain knowledge to clinical predictive models
Clinical predictive models are useful in predicting a patient's risk of developing adverse outcomes and in guiding patient therapy. In this thesis, we explored two different ways to apply domain knowledge to improve clinical predictive models. We first applied knowledge about the heart to engineer …
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Transfer learning for predictive models in MOOCs
Predictive models are crucial in enabling the personalization of student experiences in Massive Open Online Courses. For successful real-time interventions, these models must be transferable - that is, they must perform well on a new course from a different discipline, a different context, or even …
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Localised Near Horizon Predictive Models of Cellular Load
… management techniques will benefit from detailed predictive models of network load to allow for the preallocation of network parameters and resources. This thesis uses anonymised Call Detail Records (CDR) from Meteor, a mobile network provider in the Republic of Ireland, to model network load and …
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Experimental/analytical predictive models of damped structural dynamics
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1993.
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Predictive models of procedural human supervisory control behavior
… for proper system operation. In particular, such models can help support the decision making process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected. By exploiting the operator behavioral patterns which are typically reinforced through …
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Predictive models for power dissipation in optical transceivers
… generation process cards from the Berkeley Predictive Technology model, and enable the simulations to predict the power dissipation of the MUXs in the future. The results of these SPICE simulations show that improvement in technology generations significantly reduces the power dissipation of …
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Blunt Force Trauma to the Ribs: Creating Predictive Models
Forensic anthropologists receive more requests for trauma analysis than any other aspect of the biological profile. Blunt force trauma to the ribs is some of the most common trauma recorded in a medical examiner's setting, however the structural complexity of ribs make it difficult to move beyond …
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The Many Types of Churn and Their Predictive Models
… are applied and compared in terms of multiple predictive performance measures. The random forest classification measures report the strongest performance. Additionally, the customer character variables of residential months reveal importance when conducting logistic regression and net worth …
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Multiple adaptive mechanisms for predictive models on streaming data.
… areas. Changes in data may cause a decrease in predictive accuracy, which in a streaming setting require a prompt response. In recent years many adaptive predictive models have been proposed for dealing with these issues. Most of these methods use more than one adaptive mechanism, deploying all …
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A structural after measurement approach to bifactor predictive models
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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Evaluating Predictive Models for Predicting Total Score of Beef Carcasses
… of market success. This thesis examines predictive modeling techniques for estimating the Total Score of beef carcasses, a composite measure representing yield and quality, primarily used by the Nebraska Cattlemen Association. Using data from the Nebraska Cattlemen’s Foundation Retail …
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Automatic and adaptive preprocessing for the development of predictive models.
… unknown values. There exists a multitude of predictive models for the most common tasks of classification and regression. However, researchers often assume that data is clean and far too little attention has been paid to data pre-processing. Despite the fact that there are a number of methods …
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Predictive Models for Visuomotor Feedback Control in Object Pile Manipulation
… our observations? In addition, how can we build predictive models over such representations that can be useful for their task-free generality? In the first chapter of this thesis, we investigate using the image observation directly as state, and compare different models that can be useful over …
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Using genetic programming to learn predictive models from spatio-temporal data
… thesis describes a novel technique for learning predictive models from nondeterministic spatio-temporal data. The prediction models are represented as a production system, which requires two parts: a set of production rules, and a conflict resolver. The production rules model different, typically …
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Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
… the NIRS useful for analysis. Different types of predictive models such as linear, nonlinear, and hybrid predictive models were commonly used to predict component of interest from NIRS spectral data. However, different predictive model approached may achieve different accuracy of performance in …
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Forest dynamics at regional scales: predictive models constrained with inventory data
… changing climate is uncertain. Forest simulation models make landscape-level predictions of forest dynamics by scaling from key tree-level processes, but models typically have no climate dependency. In this thesis I demonstrate how large-scale national inventories combined with improvements in …
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Using Predictive Models to Identify Trends Among Successful Dual-Use Startups
This study examines predictive models for assessing the success of dual-use startups in the United States. Utilizing data from the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs, this research focused on startups founded post-2000 to reflect …
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