Prototype-based classifiers such as learning vector quantization (LVQ) often display intuitive and flexible classification and learning rules. However, classical techniques are restricted to vectorial data only, and hence not suited for more complex data structures. Therefore, a few extensions of diverse LVQ variants to more general data which are characterized based on pairwise similarities or...

The goal of cluster analysis is to separate a set of objects into constituent groups so that the members of any one group diier from one another as little as possible, according to a given criteria 6]. Pal et al. 5] proposed a generalized learning vector quantiza-tion (GLVQ) algorithm and compared it with learning vector quantization (LVQ) algorithm on clustering Anderson's IRIS data 3]. In thi...

This research effort deals with the application of Artificial Neural Networks (ANNs) in order to help the diagnosis of cases with an orthopaedic disease, namely osteoporosis. Probabilistic Neural Networks (PNNs) and Learning Vector Quantization (LVQ) ANNs, were developed for the estimation of osteoporosis risk. PNNs and LVQ ANNs are both feed-forward networks; however they are diversified in te...

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT image...

This paper describes two experiments of applying dialogue act recognition to estonian dialogues. Two class systems were used in both of them—a general one (with 19 classes) and a detailed one (with 107 classes). In the first experiment the task was performed using learning vector quantization (LVQ). The preprocessing was done in WEBSOM (Self-Organizing Maps for Internet Exploration) style; the ...