Global ETD Search
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Showing 1 to 6 of 6 for “"Automatic machine learning"”.
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Meta-Feature Taxonomy for Supporting Automatic Machine Learning
Many automatic machine learning (AutoML) libraries have been developed recently, meeting public demand for more machine learning tools which can be used without an expert. A common tactic illicited by these frameworks is to initially generate meta-features which are then used as an initial …
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Total Organic Carbon and Clay Estimation in Shale Reservoirs Using Automatic Machine Learning
… the "sweet spots" in shale gas plays. Recently, machine learning has been proved to be effective to estimate TOC and clay from well loggings. The remaining questions are what algorithm we should choose in the first place and whether we can improve the already built models. Automatic machine …
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Meta-level learning for the effective reduction of model search space.
… computational resources to find the mapping of learning methods that leads to the optimized performance on a given task. Moreover, numerous configurations of these learning algorithms add another level of complexity. Thus, it triggers the need for an intelligent recommendation engine that can …
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AMD, analysis of mood dysregulation : a machine learning approach
… environment. This research presents a new automatic machine learning pipeline, called Analysis of Mood Dysregulation (AMD), which is used to assess mood or emotional dysregulation caused by underlying psychological disorders, environmental factors, and daily activities. The data is …
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Gaining Perspective with an Evolutionary Cognitive Architecture for Intelligent Agents
… the creation of strong AI using AutoML (Automatic Machine Learning) through the development of a general cognitive architecture called Brain Evolver. To do this, the notion of what intelligence is in the context of machines and how it can practically be applied to physical intelligent …
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Structure-aware Deep Learning
… systems designed to process this data, in their learning algorithms and the very nature of the tasks they solve. At the same time, machine learning methods are extremely data-hungry, requiring petabytes of data for training. Due to their complexity, graphs remain an under-utilized resource in …