Estimating individual tree growth with the k-nearest neighbour and k-Most Similar Neighbour methods

Estimating Individual Tree Growth with the k-Nearest Neighbour and k-Most Similar Neighbour Methods

The purpose of this study was to examine the use of non-parametric methods in estimating tree level growth models. In non-parametric methods the growth of a tree is predicted as a weighted average of the values of neighbouring observations. The selection of the nearest neighbours is based on the differences between tree and stand level characteristics of the target tree and the neighbours. The ...

k-Nearest Neighbour Classifiers

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such...

COM3401 Individual Project SpamKANN: A k-Nearest Neighbour Spam Filter

Fast k nearest neighbour search for R-tree family

A simpliied k nearest neighbour (knn) search for the R-tree 6] family is proposed in this paper. This method is modiied from the technique developed by Roussopoulos et al. 10]. The main approach aims to eliminate redundant searches when the data is highly correlated. We also describe how MINMAXDIST calculations can be avoided using MINDIST as the only distance metric which gives a signiicant sp...

Extensions of the k Nearest Neighbour Methods for Classification Problems

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...