We propose a method to automatically determine the relevance of the input dimensions of a learning vector quantization (LVQ) architecture during training. The method is based on Hebbian learning and introduces weighting factors of the input dimensions which are automatically adapted to the speci c problem. The bene ts are twofold: On the one hand, the incorporation of relevance factors in the L...

A new compression algorithm for ngerprint images is introduced. Using Lattice Vector Quantization (LVQ), a technique for determining the largest radius of the Lattice and its scaling factor is presented. The design is based on obtaining the smallest possible Expected Total Distortion (ETD) measure, using a given bit budget, while using the smallest codebook size. In the proposed Piecewise-Unifo...

A new dynamic strategy for Kohonen’s LVQ algorithms using growing and pruning methods is introduced. Once the learning system converges to a solution using a fixed number of prototypes, the growing method incrementally adds new prototypes in those local regions where the misclassification error is greater. The initial locations of the new prototypes are viewed as estimates of the new equilibriu...

A new system for selection of reference instances, which is called the EkP system (Exactly k Prototypes), has been introduced by us recently. In this paper we study suitability of the EkP method for training data reduction on seventeen datasets. As the underlaying classifier the well known IB1 system (1-Nearest Neighbor classifier) has been chosen. We compare generalization ability of our metho...

This paper presents a novel texture-based algorithm for detecting certain kinds of meningiomas in images of neurosurgical resections. The algorithm employs Discriminant Wavelet Packet Transform (DWPT) and Learning Vector Quantization (LVQ). The adaptive DWPT of a test image is computed by maximizing the discrimination power of subbands during the basis selection process for the training images....

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...

We consider images of boar spermatozoa obtained with an optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-reacted (class 2). Such classification is important for the estimation of the fertilization potential of a sperm sample for artificial insemination. We segment the sperm heads and compute a feature vector for...

This paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach. The character classi2cation is achieved by combining the use of neural gas (NG) and learning vector quantization (LVQ). NG is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not....

Data mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector quantization (LVQ), single-layer perceptron (SLP) and support vector machines (SVM) were put forward for multi-wavelength data classification. A feature selection ...