on this thesis. My grandmother Lydia, my sister Petra and her husband Thomas merit my sincere and loving thanks for they have given me a lot of encouragement and strength. Many thanks to all my friends and colleagues for their support during studying, like Michael Dittenbach

In this paper we present the growing hierarchical self-organizing map. This dynamically growing neural network model evolves into a hierarchical structure according to the requirements of the input data during an unsupervised training process. We demonstrate the benefits of this novel neural network model by organizing a real-world document collection according to their similarities.

With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. In this paper we present the PlaySOM, as well as the PocketSOMPlayer, two novel interfaces allowing to browse a music collection by navigating a map of clustered music tracks and to select regions of interest containing similar tracks for playing. The PlaySOM system is primarily designed to allow interaction via a large-screen device, whereas the PocketSOMPlayer is implemented for mobile devices, supporting both local as well as streamed audio replay. This approach offers content-based organization of music as an alternative to conventional navigation of audio archives, i.e. flat or hierarchical listings of music tracks that are sorted and filtered by meta information.

The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related, on the one hand, to the static architecture of this model, as well as, on the other hand, to the limited capabilities for the representation of hierarchical relations of the data. With our novel Growing Hierarchical SelfOrganizing Map presented in this paper we address both limitations. The Growing Hierarchical Self-Organizing Map is an arti cial neural network model with hierarchical architecture composed of independent growing self-organizing maps. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The bene ts of this novel neural network are rst, a problem-dependent architecture, and second, the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.

Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding

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We present our recent work on the Growing Hierarchical Self-Organizing Map, a dynamically growing neural network model which evolves into a hierarchical structure according to the necessities of the input data during an unsupervised training process. The benefits of this novel architecture are shown by organizing a real-world document collection according to semantic similarities.

In this paper we present a multilingual information retrieval system that provides access to Tourism in-formation by exploiting the intuitiveness of natural language. In particular, we describe the knowledge representation model underlying the information re-trieval system. This knowledge representation ap-proach is based on associative networks and allows the definition of semantic relationships between domain-intrinsic information items. The network structure is used to define weighted associations between in-formation items and augments the system with a fuzzy search strategy. This particular search strat-egy is performed by a constrained spreading activa-tion algorithm that implements information retrieval on associative networks. Strictly speaking, we take the relatedness of terms into account and show, how this fuzzy search strategy yields beneficial results and, moreover, determines highly associated matches to users ’ queries. Thus, the combination of the associa-tive network and the constrained spreading activation approach constitutes a search algorithm that evalu-ates the relatedness of terms and, therefore, provides a means for implicit query expansion.