In previous work we reported high classiication rates for Learning Vector Quantization (LVQ) networks trained to classify phoneme tokens shifted in time. It has since been shown that the framework of Minimum Classiication Error (MCE) and Generalized Probabilistic Descent (GPD) can treat LVQ as a special case of a general method for gradient descent on a rigorously deened classiication loss meas...

Crisp and fuzzy-logic rules are used for comprehensible representation of data, but rules based on similarity to prototypes are equally useful and much less known. Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with the Learning Vector Quantization (LVQ) algorithm being a prominent examp...

Automatic Document Classification that corresponds with user-predefined classes is a challenging and widely researched area. Self-Organizing Maps (SOM) are unsupervised Artificial Neural Networks (ANN) which are mathematically characterized by transforming high-dimensional data into two-dimension representation, enabling automatic clustering of the input, while preserving higher order topology....

This work presents training methods and recogni tion experiments for phoneme wise tied mixture den sities in hidden Markov models HMM The system trains speaker dependent but vocabulary independent phoneme models for the recognition of Finnish words The Learning Vector Quantization LVQ methods are applied to increase the discrimination between the phoneme models A segmental LVQ training is pro p...

Learning vector quantization (LVQ) as proposed by Kohonen is a simple and intuitive, though very successful prototype—based clustering algorithm.Generalized relevance LVQ (GRLVQ) constitutes a modification which obeys the dynamics of a gradient descent and allows an adaptive metric utilizing relevance factors for the input dimensions. As iterative algorithms with local learning rules, LVQ and m...

Color-segmentation is quite sensitive to changes in light intensity. Many algorithms do not tolerate variations in color hue which correspond, in fact, to the same object. In this work an image segmentator algorithm based on Learning Vector Quantization (LVQ) networks is proposed and tested on a tracking application. In LVQ networks, neighboring neurons learn to recognize neighboring sections o...

ارتقای بازدارندگی دفاعی مستلزم تولید گسترده و متنوع محصولات تجهیزات نظامی است که نیازمند به‌کارگیری ظرفیت‌ها قابلیت‌های وسیع صنعتی است. ازآنجاکه تصدی تملک همه این قابلیت‌ها منطقی اقتصادی نیست، بخشی از فعالیت‌های تولیدی در صنایع دفاعی، برون‌سپاری می­‌شود به ایجاد شبکه بزرگی تأمین‌کنندگان منجر شده گستردگی شبکه، تعاملات همکارانه بهبود عملکرد زنجیره تأمین را یک چالش جدی برای مدیران تبدیل کرده سازما...

Metric learning constitutes a well-investigated field for vectorial data with successful applications, e.g. in computer vision, information retrieval, or bioinformatics. One particularly promising approach is offered by lowrank metric adaptation integrated into modern variants of learning vector quantization (LVQ). This technique is scalable with respect to both, data dimensionality and the num...

Five existing LVQ algorithms are reviewed. The Premature Clustering Phenomenon, which downgrades the performance of LVQ is explained. By introducing and applying the “equalizing factor” as a remedy for the premature clustering phenomenon a breakthrough is achieved in improving the performance of the LVQ network, and its performance becomes competitive with that of the best known classi6ers. For...