Weighted Instance Typicality Search (WITS): A nearest neighbor data reduction algorithm

Weighted Instance Typicality Search (WITS): A nearest neighbor data reduction algorithm

Two disadvantages of the standard nearest neighbor algorithm are 1) it must store all the instances of the training set, thus creating a large memory footprint and 2) it must search all the instances of the training set to predict the classification of a new query point, thus it is slow at run time. Much work has been done to remedy these shortcomings. This paper presents a new algorithm WITS (...

Nearest Neighbor Search Algorithm

A fundamental activity common to image processing, pattern recognition, and clustering algorithm involves searching set of n , k-dimensional data for one which is nearest to a given target data with respect to distance function . Our goal is to find search algorithms with are full search equivalent -which is resulting match as a good as we could obtain if we were to search the set exhausting. 1...

Winner-Update Algorithm for Nearest Neighbor Search

This paper presents an algorithm, called the winnerupdate algorithm, for accelerating the nearest neighbor search. By constructing a hierarchical structure for each feature point in the lp metric space, this algorithm can save a large amount of computation at the expense of moderate preprocessing and twice the memory storage. Given a query point, the cost for computing the distances from this p...

An Instance Selection Algorithm Based on Reverse Nearest Neighbor

K- Nearest Neighbor Algorithm for Instance Based Learning

Instance Based Learning (IBL) results in classifying a new instance by examining and comparing it to the rest of the instances in the dataset. An example of this type of learning is the K-Nearest Neighbor algorithm which is based on examining an average Euclidian distance of the nearest k neighbors' parameters given a certain situation.