single nearest neighbour fuzzy approach 2 ABSTRACT The main aim of this paper is to introduce the single nearest neighbour approach for pattern recognition and the concept of incremental learning of a fuzzy classifier where decision making is based on data available up to time t rather than what may be available at the start of the trial, i.e. at t=0. The single nearest neighbour method is expl...

In this paper, Multi-Layer Perceptron and RadialBasis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3and 6-h lead time prediction and the only reliable one for 9-h lead time forecas...

The automatic categorisation of web documents is becoming crucial for organising the huge amount of information available in the Internet. We are facing a new challenge due to the fact that web documents have a rich structure and are highly heterogeneous. Two ways to respond to this challenge are (1) using a representation of the content of web documents that captures these two characteristics ...

We derive a new asymptotic expansion for the global excess risk of a local k-nearest neighbour classifier, where the choice of k may depend upon the test point. This expansion elucidates conditions under which the dominant contribution to the excess risk comes from the locus of points at which each class label is equally likely to occur, but we also show that if these conditions are not satisfi...

Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest neighbour and case-based reasoning. Of these two methods, nearest neighbour fell into disfavour during the 1980s, but regained popularity recently due to its simplicity and ease of implementation. Nearest...

In this paper, we propose a nearest neighbour algorithm that uses the lower and upper approximations from fuzzy rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other nearest neighbour approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction ...

Let G = Gn,k denote the graph formed by placing points in a square of area n according to a Poisson process of density 1 and joining each point to its k nearest neighbours. In [2] Balister, Bollobás, Sarkar and Walters proved that if k < 0.3043 logn then the probability that G is connected tends to 0, whereas if k > 0.5139 logn then the probability that G is connected tends to 1. We prove that,...

Shape is probably the single most important feature for object detection and much research has gone into developing deformable shape models. However, contours extracted by bottom-up edge detectors are notoriously unreliable, especially in natural images. In this paper, we present a very simple, yet powerful method for model creation, hypothesis generation, and hypothesis verification, which is ...

Computer-aided detection and diagnosis (CAD) schemes have been developed and applied to detect suspicious lesions depicted on biomedical images. After identifying initial candidates for the targeted suspicious lesions, most CAD schemes use a pre-trained multi-image-feature based machine learning classifier to classify these candidates into two groups of positive and negative detections. Althoug...

This article proposes an optimized instance-based learning approach for prediction of the compressive strength of high performance concrete based on mix data, such as water to binder ratio, water content, super-plasticizer content, fly ash content, etc. The base algorithm used in this study is the k nearest neighbor algorithm, which is an instance-based machine leaning algorithm. Five different...