Abstract Electricity is vital energy for the sustainability of human activities both as individuals, community groups, and industrial world. users are increasing every year, which causes irresponsible does not comply with existing rules; number staff to find it challenging determine whether power used appropriate household needs. This study uses data on 100 electricity obtained from PT. PLN Ray...

We describe a kernel method which uses the maximization of Onicescu’s informational energy as a criteria for computing the relevances of input features. This adaptive relevance determination is used in combination with the neural-gas and the generalized relevance LVQ algorithms. Our quadratic optimization function, as an L type method, leads to linear gradient and thus easier computation. We ob...

We are concerned with the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by he rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods: Limited Rank Matrix Learning Vector Quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) app...

Abstract We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types systems: prototype-based vector quantization (LVQ) classification and shallow, layered neural networks regression tasks. investigate so-called student–teacher scenarios which systems are trained from stream high-dimensional, labeled data...

Due to its intuitive learning algorithms and classification behavior, learning vector quantization (LVQ) enjoys a wide popularity in diverse application domains. In recent years, the classical heuristic schemes have been accompanied by variants which can be motivated by a statistical framework such as robust soft LVQ (RSLVQ). In its original form, LVQ and RSLVQ can be applied to vectorial data ...

This paper presents some results on the possibilities offered by neural networks for human face recognition. In particular, two algorithms have been tested: learning vector quantization (LVQ) and multilayer perceptron (MLP). Two different approaches have been taken for each case, using as input data either preprocessed images (gray level or segmented), or geometrical features derived from a set...