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Beschreibung:
We propose an innovative machine learning algorithm called incremental import vector machines that is used for classification purposes. The classifier is specifically designed for the task of sequential learning, in which the data samples are successively presented to the classifier. The motivation for our work comes from the effort to formulate a classifier that can manage the major challenges of sequential learning problems, while being a powerful classifier in terms of classification accuracy, efficiency and meaningful output. One challenge of sequential learning is that data samples are not completely available to the learner at a given point of time and generally, waiting for a representative number of data is undesirable and impractical. Thus, in order to allow for a classification of given data samples at any time, the learning phase of the classifier model needs to start immediately, even if not all training samples are available. Another challenge is that the number of sequential arriving data samples can be very large or even infinite and thus, not all samples can be stored. Furthermore, the distribution of the sample can vary over time and the classifier model needs to remain stable and unchanged to irrelevant samples while being plastic to new, important samples. Therefore our key contribution is to develop, analyze and evaluate a powerful incremental learner for sequential learning which we call incremental import vector machines (I 2 VMs). The classifier is based on the batch machine learning algorithm import vector machines, which was developed by Zhu and Hastie (2005). I 2 VM is a kernel-based, discriminative classifier and thus, is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. A key achievement of this thesis is the verification and analysis of the discriminative and reconstructive model components of IVM and I 2 VM. While discriminative classifiers try to separate the classes as well as ...