This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the wellknown fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results...

This paper demonstrates classification of PQ events utilizing wavelet transform (WT) energy features by artificial neural network (ANN) and SVM classifiers. The proposed scheme utilizes wavelet based feature extraction to be used for the artificial neural networks in the classification. Six different PQ events are considered in this study. Three types of neural network classifiers such as feed ...

This paper proposes the use of neural network classifiers to evaluate back propagation (BP) and learning vector quantization (LVQ) for feature selection of load identification in a non-intrusive load monitoring (NILM) system. To test the performance of the proposed approach, data sets for electrical loads were analyzed and established using a computer supported program Electromagnetic Transient...

This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ ) algorithm , this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE...

This paper shows that the distributed representation found in Learning Vector Quantization (LVQ) enables reinforcement learning methods to cope with a large decision search space, defined in terms of equivalence classes of input patterns like those found in the game of Go. In particular, this paper describes S[arsa]LVQ, a novel reinforcement learning algorithm and shows its feasibility for patt...

The basic concepts of distance based classification are introduced in terms of clear-cut example systems. The classical k-NearestNeigbhor (kNN) classifier serves as the starting point of the discussion. Learning Vector Quantization (LVQ) is introduced, which represents the reference data by a few prototypes. This requires a data driven training process; examples of heuristic and cost function b...

Learning Vector Quantization (LVQ) [1] is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) [2] is a promising one. Although LVQ methods have an intuitive design with clear updating rules, their dynamics are not yet well understood. In simulations within a controlled environment RSLVQ p...

A decision approach to mechanism type selection is presented, which employs LVQ neural network as classifier and decision-maker to recognize a satisfactory mechanism from a range of mechanisms achieving a required kinematic function. Through learning from correct samples extracted from different mechanisms, expert knowledge is acquired and expressed in the form of weight matrix by LVQ network. ...

We present a regularization technique to extend recently proposed matrix learning schemes in Learning Vector Quantization (LVQ). These learning algorithms extend the concept of adaptive distance measures in LVQ to the use of relevance matrices. In general, metric learning can display a tendency towards over-simplification in the course of training. An overly pronounced elimination of dimensions...