Machine learning plays an increasingly important role in modern systems. The ability to learn from data enhances or enables many applications. Recently, quick in-stream processing of possibly a huge or even infinite amount of data gains more attention. This thesis deals with such on-line learning systems for regression that learn with every example incrementally and are reliable even in presence of uncertainties. A new learning approach, called IRMA, is introduced which directly incorporates knowledge about the model structure into its parameter update. This way it is aggressive to incorporate a new example locally as much as possible and at the same time passive in the sense that the global output is changed as little as possible. It can be applied to any model structure that is linear in its parameters and is proven to minimize the worst case prediction error in each step. Hence, IRMA is reliable in every situation and the investigations show that in every case a bad performance is prevented by inherently averting overfitting even for complex model structures and in high dimensions. An extension of such on-line learning systems monitors the learning process, regarding conflict and ignorance, and estimates the trustworthiness of the learned hypothesis by the means of trust management. This provides insight into the learning system at every step and the designer can adjust its setup if necessary. Additionally, the trust estimation allows to assign a trustworthiness to each individual prediction the learning system makes. This way the overall system can react to uncertain predictions at a higher level and increase its safety, e.g. by reverting to a fallback. Furthermore, the uncertainties are explicitly incorporated into the learning process. The uncertainty of the hypothesis is reflected by allowing less change for more certain regions of the learned system. This way, good learned knowledge is protected and a higher robustness to disturbances is achieved. The uncertainty of each example used for learning is reflected by adapting less to uncertain examples. Thereby, the learning system gets more robust to training examples that are known to be uncertain. All approaches are formally analyzed and their characteristic properties are demonstrated in empirical investigations. In addition, a real world application to forecasting electricity loads shows the benefits of the approaches.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:repositorium.ub.uni-osnabrueck.de:urn:nbn:de:gbv:700-2014101512888 |
| Date | 15 October 2014 |
| Creators | Buschermöhle, Andreas |
| Contributors | Prof. Dr. Werner Brockmann, Prof. Dr. Eyke Hüllermeier |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf, application/zip |
| Rights | Namensnennung-NichtKommerziell-KeineBearbeitung 3.0 Unported, http://creativecommons.org/licenses/by-nc-nd/3.0/ |
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