Predicting Customer Behaviour Using Self-Oganizing Map and Comparing with K Nearest Neighbour

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...

Generalized K-Nearest Neighbour Algorithm- A Predicting Tool

k-nearest neighbour algorithm is a non-parametric machine learning algorithm generally used for classification. It is also known as instance based learning or lazy learning. K-NN algorithm can also be adapted for regression that is for estimating continuous variables. In this research paper the researcher endow with a generalized K-nearest algorithm used for predicting a continuous value. In or...

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 ...

Localization using a Region-Based k-Nearest Neighbour Search

This paper explores a method of performing localization on local vision mobile robots. It describes a method of processing images, extracting regions, and comparing those regions against a database of preprocessed images. Localization is achieved using the k-nearest neighbour algorithm as the basis for approximating the current position. Initial results are provided that show the potential of t...

Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour