Sökning: "random decision forests"
Visar resultat 1 - 5 av 7 avhandlingar innehållade orden random decision forests.
1. Learning Decision Trees and Random Forests from Histogram Data : An application to component failure prediction for heavy duty trucks
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
2. Random Forest for Histogram Data : An application in data-driven prognostic models for heavy-duty trucks
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. Combining Shape and Learning for Medical Image Analysis
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
4. Improving Multi-Atlas Segmentation Methods for Medical Images
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
5. Scalable Machine Learning through Approximation and Distributed Computing
Sammanfattning : Machine learning algorithms are now being deployed in practically all areas of our lives. Part of this success can be attributed to the ability to learn complex representations from massive datasets. LÄS MER