This thesis proposes a classifier combination system in the context of traversability classification. The goal is to enable a system to utilize on-line training for a fast adaptation to new and changing environments. Moreover the on-line classifier is able to handle additional context information that is only available in the on-line environment. Therefore the learning system is based on two di...

Appropriately combining information sources to form a more effective output than any of the individual sources is a broad topic that has been researched in many forms. It can be considered to contain sensor fusion, distributed data-mining, regression combination, classifier combination, and even the basic classification problem. After all, the hypothesis a classifier emits is just a specificati...

We propose a new statistical method for learning normalized confidence values in multiple classifier systems. Our main idea is to adjust confidence values so that their nominal values equal the information actually conveyed. In order to do so, we assume that information depends on the actual performance of each confidence value on an evaluation set. As information measure, we use Shannon’s well...

Classifier combination is a technique that often provides significant improvements in accuracy, and also furnishes a useful mechanism to support multi-modal information sources. In this paper we discuss the problem of acoustic classifier combination in speech recognition systems. We present new techniques that generalize previously used combination rules, such as the mean, product, min, and max...

the article suggests an algorithm for regular classifier ensemble methodology. the proposed methodology is based on possibilistic aggregation to classify samples. the argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. the optimization aims at learning backgrounds as solid clusters in subspaces of the high-dim...

Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (<4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or nove...