In this chapter, one of the most popular and intuitive prototype-based classification algorithms, learning vector quantization (LVQ), is revisited, and recent extensions towards automatic metric adaptation are introduced. Metric adaptation schemes extend LVQ in two aspects: on the one hand a greater flexibility is achieved since the metric which is essential for the classification is adapted ac...

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) ...

This paper is the extension of two-stage vector quantization–(spherical) lattice vector quantization (VQ–(S)LVQ) recently introduced by Pan and Fischer [1]. First, according to high resolution quantization theory, generalized vector quantization–lattice vector quantization (G-VQ–LVQ) is formulated in order to release the constraint of the spherical boundary for the second-stage lattice vector q...

Web 2.0 services have enabled people to express their opinions, experience and feelings in the form of user-generated content. Sentiment analysis or opinion mining involves identifying, classifying and aggregating opinions as per their positive or negative polarity. This paper investigates the efficacy of different implementations of Self-Organizing Maps (SOM) for sentiment based visualization ...

Semi-supervised learning (SSL) is focused on learning from labeled and unlabeled data by incorporating structural and statistical information of the available unlabeled data. The amount of data is dramatically increasing, but few of them are fully labeled, due to cost and time constraints. This is even more challenging for non-vectorial, proximity data, given by pairwise proximity values. Only ...

Neural maps and Learning Vector Quantizer are fundamental paradigms in neural vector quantization based on Hebbian learning. The beginning of this field dates back over twenty years with strong progress in theory and outstanding applications. Their success lies in its robustness and simplicity in application whereas the mathematics beyond is rather difficult. We provide an overview on recent ac...

In this paper we consider a feature extraction approach for recognition of handwritten electrical symbols. The symbols are represented as a sequence of points. We apply a feature extraction technique to extract the most important features and then feed them for recognition to a Neural Network. We utilize a Learning Vector Quantization (LVQ) network and show its capability to recognize the symbo...

We propose an algorithm for training Multi Layer Preceptrons for classification problems, that we named Hidden Layer Learning Vector Quantization (H-LVQ). It consists of applying Learning Vector Quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of ...

This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on multiple weighted norms to measure the distance between the feature vectors and their prototypes. Clustering and LVQ are formulated in this paper as the minimization of a reformulation function that employs distinct weighted norms to measure the distance between each of the prot...