Sökning: "Random Forests"

Visar resultat 1 - 5 av 24 avhandlingar innehållade orden Random Forests.

1. 1. Random Forest for Histogram Data : An application in data-driven prognostic models for heavy-duty trucks

   Författare :Ram Bahadur Gurung; Henrik Boström; Tony Lindgren; Niklas Lavesson; Stockholms universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Histogram data; random forest; NOx sensor failure; random forest interpretation; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : 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. LÄS MER

3. 2. Order in the random forest

   Författare :Isak Karlsson; Henrik Boström; Lars Asker; Pierre Geurts; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine learning; random forest; ensemble; time series; data series; sequential data; sparse data; high-dimensional data; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : 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. LÄS MER

4. 3. Learning Decision Trees and Random Forests from Histogram Data : An application to component failure prediction for heavy duty trucks

   Författare :Ram Bahadur Gurung; Henrik Boström; Tony Lindgren; Stockholms universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; histogram decision trees; histogram random forest; prognostics; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : 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. LÄS MER

5. 4. Combining Shape and Learning for Medical Image Analysis

   Författare :Jennifer Alvén; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; feature-based registration; convolutional neural networks; conditional random fields; medical image segmentation; random decision forests; machine learning; multi-atlas segmentation; medical image registration; shape models;

   Sammanfattning : 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. LÄS MER

7. 5. Improving Multi-Atlas Segmentation Methods for Medical Images

   Författare :Jennifer Alvén; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Supervised learning; semantic segmentation; multi-atlas segmentation; conditional random fields; label fusion; feature-based registration; image registration; random decision forests; convolutional neural networks; medical image segmentation;

   Sammanfattning : 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. LÄS MER