Search for dissertations about: "Random Forests"
Showing result 1 - 5 of 24 swedish dissertations containing the words Random Forests.
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1. Random Forest for Histogram Data : An application in data-driven prognostic models for heavy-duty trucks
Abstract : Data mining and machine learning algorithms are trained on large datasets to find useful hidden patterns. These patterns can help to gain new insights and make accurate predictions. Usually, the training data is structured in a tabular format, where the rows represent the training instances and the columns represent the features of these instances. READ MORE
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2. Order in the random forest
Abstract : In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. READ MORE
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3. Learning Decision Trees and Random Forests from Histogram Data : An application to component failure prediction for heavy duty trucks
Abstract : A large volume of data has become commonplace in many domains these days. Machine learning algorithms can be trained to look for any useful hidden patterns in such data. Sometimes, these big data might need to be summarized to make them into a manageable size, for example by using histograms, for various reasons. READ MORE
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4. Combining Shape and Learning for Medical Image Analysis
Abstract : Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. READ MORE
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5. Improving Multi-Atlas Segmentation Methods for Medical Images
Abstract : Semantic segmentation of organs or tissues, i.e. delineating anatomically or physiologically meaningful boundaries, is an essential task in medical image analysis. One particular class of automatic segmentation algorithms has proved to excel at a diverse set of medical applications, namely multi-atlas segmentation. READ MORE