SOM and LVQ algorithms for symbol strings have been introduced and applied to isolatedword recognition, for the construction of an optimal pronunciation dictionary for a given speech recognizer.

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...