This paper proposes a novel method for supervised subspace learning based on Single-hidden Layer Feedforward Neural networks. The proposed method calculates appropriate network target vectors by formulating a Bayesian model exploiting both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by t...

The rapid exponential growth in wireless technologies and the need for public safety has led to increasing demand location-based services. Terrestrial cellular networks can offer acceptable position estimation users that meet statutory requirements set by Federal Communications Commission case of network-based positioning, regulations. In this study, proposed radio frequency pattern matching (R...

The Approximating and Eliminating Search Algorithm (AESA) and related AESA-based techniques are among the fastest methods for (k-)Nearest Neighbour(s) searching in general metric spaces. These techniques can be optimized for the (easier) (k-)Nearest Neighbour(s) classification problem. In particular, an optimized version of the AESA is proposed here which is shown to be significantly faster tha...

In this paper we describe the parallelization of two nearest neighbour classification algorithms. Nearest neighbour methods are well-known machine learning techniques. They have been successfully applied to Text Categorization task. Based on standard parallel techniques we propose two versions of each algorithm on message passing architectures. We also include experimental results on a cluster ...

The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a dataset. The results of our experiments using datasets from the UCI repository demonstrate that the c...

Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical viewpoints. These problems have motivated the hybridisation of fuzzy-rough sets with kernel methods. Existing work which hybridises fuzzy-rough sets and kernel methods emp...

This paper is based on the observation that the nearest neighbour in a case-based prediction system may not be the best case to explain a prediction. This observation is based on the notion of a decision surface (i.e. class boundary) and the idea that cases located between the target case and the decision surface are more convincing as support for explanation. This motivates the idea of explana...

In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification algorithm, as an alternative to Sarkar’s fuzzyrough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the expe...

The (k-)nearest neighbour problem is well known in a wide range of areas. Many algorithms to tackle this problem suffer from the “curse of dimensionality” which means that the execution time grows exponentially with increasing dimension. Therefore, it is important to have efficient algorithms for the problem. In this report, some well known tree-based algorithms for the k-nearest neighbour are ...

In a vector quantisation (VQ) based speaker identification system, a speaker model is created for each speaker from the training speech data by using the k-means clustering algorithm. For an unknown utterance analysed into a sequence of vectors, the nearest prototype classifier is used to identify speaker. To achieve the higher speaker identification accuracy, a fuzzy approach is proposed in th...