The main aim of this short paper is to propose a new branch prediction approach called by us "neural branch prediction". We developed a first neural predictor model based on a simple neural learning algorithm, known as Learning Vector Quantization algorithm. Based on a trace driven simulation method we investigated the influences of the learning step and training processes. Also we compared the...

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...

In this paper we propose a fast online Kernel SVM algorithm under tight budget constraints. We propose to split the input space using LVQ and train a Kernel SVM in each cluster. To allow for online training, we propose to limit the size of the support vector set of each cluster using different strategies. We show in the experiment that our algorithm is able to achieve high accuracy while having...

Person identification technology has many applications. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for person identification. In this paper, a kind of event related potential-P300, is employed as the input of the identification system. Compared with the other EEG signal, the P300 wave is eas...

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 ...

The statistical physics analysis of offline learning is applied to cost function based learning vector quantization (LVQ) schemes. Typical learning behavior is obtained from a model with data drawn from high dimensional Gaussian mixtures and a system of two or three competing prototypes. The analytic approach becomes exact in the limit of high training temperature. We study two cost function re...

Relevance Learning Vector Quantization (RLVQ) (introduced in [1]) is a variation of Learning Vector Quantization (LVQ) which allows a heuristic determination of relevance factors for the input dimensions. The method is based on Hebbian learning and defines weighting factors of the input dimensions which are automatically adapted to the specific problem. These relevance factors increase the over...

A high-performance biologically-inspired odor identification system is described. As a means of odor recognition, learning vector quantization (LVQ) algorithm is employed. Performance improvement is obtained with the use of a preprocessing with discriminant analysis of input samples. Due to sample-based decision, the system can be reliably operated as a real-time electronic nose.