A typical approach in supervised learning when data comes from multiple sources is to send original data from all sources to a central location and train a predictor that estimates a certain target quantity. This can be inefficient and costly in applications with constrained communication channels, due to limited power and/or bitlength constraints. Under such constraints, one potential solution...

Abstract. We propose a semi-supervised fuzzy vector quantization method for the classification of incompletely labeled data. Since information contained within the structure of the data set should not be neglected, our method considers the whole data set during the learning process. In difference to known methods our approach uses neighborhood cooperativeness for stable prototype learning known...

The paper deals with the concept of relevance learning in learning vector quantization. Recent approaches are considered: the generalized learning vector quantization as well as the soft learning vector quantization. It is shown that relevance learning can be included in both methods obtaining similar structured learning rules for prototype learning as well as relevance factor adaptation. We sh...

This paper presents a novel method for stance and swing phase detection employing Learning Vector Quantization (LVQ), using knee angle information only. The results show detection accuracy of 95.9% in stance phase and 83.9% in swing phase. The research concludes an efficient replacement of footswitch for phase detection. The work can directly lead to low cost speed adaptive transtibial prosthes...

Prototype based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector quantization (LVQ) and extensions derived from cost functions such as generalized LVQ (GLVQ) and robust soft LVQ (RSLVQ). These methods, however, are restricted to Euclidean vectors and t...

In this study we focus on improvement performance of a cue based Motor Imagery Brain Computer Interface (BCI). For this purpose, data fusion approach is used on results of different classifiers to make the best decision. At first step Distinction Sensitive Learning Vector Quantization method is used as a feature selection method to determine most informative frequencies in recorded signals and ...