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 10 of 10 for “"Scikit-learn"”.
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ModelDB : tools for machine learning model management and prediction storage
Building a machine learning model is often an iterative process. Data scientists train hundreds of models before finding a model that meets acceptable criteria. But tracking these models and remembering the insights obtained from them is an arduous task. In this thesis, we present two main systems …
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An affordance-inspired tool for automated web page labeling and classification
… uses a gradient boosting classifier from the scikit-learn Python package to identify which of four tasks may be performed on a given web page. It also attempts to automatically label the input fields and buttons on the web page using a gradient boosting classifier. It outputs its results in a …
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Software Requirements Classification Using Word Embeddings and Convolutional Neural Networks
… <p>This thesis explores the application of deep learning techniques on software requirements classification, specifically the use of word embeddings for document representation when training a convolutional neural network (CNN). As past research endeavors mainly utilize information retrieval and …
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API Knowledge Guided Test Generation for Machine Learning Libraries
… to generate test cases for APIs of machine learning libraries by leveraging the API constraints mined from the corresponding API documentation and the API usage patterns mined from code fragments in Stack Overflow (SO). First, we propose a set of 18 linguistic rules for mining API …
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Optimization methods for parameter identifications in settings with only partial knowledge
… on incorporating prior knowledge into machine learning models. In the first project, a universal feature selection method for linear mixed-effect models is developed. Namely, Sparse Relaxed Regularized Regression (SR3) is extended to the case of Linear Mixed-Effect (LME) likelihoods, and we …
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Sonic Analysis for Machine Learning: Multi-Layer Perceptron Training using Spectrograms
… that determines what information the network has learned – for better understanding of training and trouble-shooting of such networks that have been trained to classify images. This approach spawned an unexpectedly artistic process whereby feature recognition could be used to alter images in a …
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Optimizing end-to-end machine learning pipelines for model training
… and feature transformations, and apply machine learning algorithms to train models on the preprocessed data. Existing systems can execute such end-to-end training pipelines. However, they face unique challenges in their applicability to large scale data. In particular, current approaches either …
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Criminal data analysis based on low rank sparse representation
… or even overlapping subspaces. BOTH UCI Machine Learning Repository, and crime database are the best to find and compare the best subspace clustering algorithm that fit for high dimensional space data. We used many Open-Source Machine Learning Frameworks and Tools for both employ our machine …
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SHEDDING NEW LIGHT ON OLD DATA: FINDING NEW RESULTS FOR EXOPLANET SCIENCE IN ARCHIVAL DATA
… my work on K2 light curve data using machine learning to find young stellar objects that display unusual, transit-like behaviour. These objects are known as dipper stars due to their distinctive occultations with depths of 10-50% in flux and very fast orbital periods of a few hours to a few …
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Improved In Silico Methods for Target Deconvolution in Phenotypic Screens
… to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. …