Linear Time Relational Prototype Based Learning

Patch Processing for Relational Learning Vector Quantization

Recently, an extension of popular learning vector quantization (LVQ) to general dissimilarity data has been proposed, relational generalized LVQ (RGLVQ) [10, 9]. An intuitive prototype based classification scheme results which can divide data characterized by pairwise dissimilarities into priorly given categories. However, the technique relies on the full dissimilarity matrix and, thus, has squ...

Prototypes Based Relational Learning

Relational instance-based learning (RIBL) algorithms offer high prediction capabilities. However, they do not scale up well, specially in domains where there is a time bound for classification. Nearest prototype approaches can alleviate this problem, by summarizing the data set in a reduced set of prototypes. In this paper we present an algorithm to build Relational Nearest Prototype Classifier...

Learning on Relational Data: Prototype-Based Classification of Attributed Graphs

Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets

Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural gas or the self-organizing map offer an intuitive and fast variant which represents data by means of typical representatives, thereby running in linear time. Recently, an extension of these methods towards relational c...

Prototype Learning with Attributed Relational Graphs

An algorithm for learning structural patterns given in terms of Attributed Relational Graphs (ARG’s) is presented. The algorithm, based on inductive learning methodologies, produces general and coherent prototypes in terms of Generalized Attributed Relational Graphs (GARG’s), which can be easily interpreted and manipulated. The learning process is defined in terms of inference operations especi...