In classification tasks it may be wise to combine observations from different sources. In this paper, to obtain classification systems with both good generalization performance and efficiency in space and time, a learning vector quantization learning method based on combinations of weak classifiers is proposed. The weak classifiers are generated using automatic elimination of redundant hidden l...

An algorithm is proposed to prune the prototype vectors (prototype selection) used in a nearest neighbor classifier so that a compact classifier can be obtained with similar or even better performance. The pruning procedure is error based; a prototype will be pruned if its deletion leads to the smallest classification error increase. Also each pruning iteration is followed by one epoch of Learn...

The present paper introduces an adaptive algorithm for competitive training of a nearest neighbor (NN) classifier when using a very small codebook. The new learning rule is based on the well-known LVQ method, and uses an alternative neighborhood concept to estimate optimal locations of the codebook vectors. Experiments over synthetic and real databases suggest the advantages of the learning tec...

The R-rule is a heuristic algorithm for distancebased neural network (DBNN) learning. Experimental results show that the R-rule can obtain the smallest or nearly smallest DBNNs. However, the computational cost of the R-rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively during learning. To reduce the cost of the R-rule, we investigate three appro...

This paper presents a Learning Vector Quantization (LVQ)-based temporal tracking method for semi-automatic video object segmentation. A semantic video object is initialized using user assistance in a reference frame to give initial classification of video object and its background regions. The LVQ training approximates video object and background classification and use them for automatic segmen...

Kohonen's learning vector quantization (LVQ) is modified by attributing training counters to each neuron, which record its training statistics. During training, this allows for dynamic self-allocation of the neurons to classes. In the classification stage training counters provide an estimate of the reliability of classification of the single neurons, which can be exploited to obtain a substant...

Abstract The recent increase in dimensionality of data has thrown a great challenge to the existing dimensionality reduction methods in terms of their effectiveness. Dimensionality reduction has emerged as one of the significant preprocessing steps in machine learning applications and has been effective in removing inappropriate data, increasing learning accuracy, and improving comprehensibilit...

We applied a method called Distinction-Sensitive Learning Vector Quantization (DSLVQ) to the classification of footsteps. The measurements were made by a pressure-sensitive floor, which is part of the smart sensing living room in our research laboratory. The aim is to identify walkers based on their single footsteps. DSLVQ is an extended version of Learning Vector Quantization (LVQ), and it can...

This paper describes a method for detection, tracking and recognition of lower arm and hand movements from color video sequences using a linguistic approach driven by motion analysis and clustering techniques. The novelty of our method comes from (i) automatic arm detection, without any manual initialization, foreground or background modeling, (ii) gesture representation at different levels of ...

A method for detecting the facial feature points, such as the pupil, subnasal point, and corners of the mouth, is proposed. The proposed method is composed of two stages: candidate detection of facial feature points and optimization of these points by using a facial shape model. The candidates for each facial-feature-point are extracted from a face image by using generalized learning vector qua...