Proximity searching consists in retrieving from a database, objects that are close to a query. For this type of searching problem, the most general model is the metric space, where...
—A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors’ distances ...
The Support vector machines derive the class decision hyper planes from a few, selected prototypes, the support vectors (SVs) according to the principle of structure risk minimizat...
Given a metric space (X, dX), c ≥ 1, r > 0, and p, q ∈ [0, 1], a distribution over mappings H : X → N is called a (r, cr, p, q)-sensitive hash family if any two points in...
The nearest neighbor (NN) classifier is well suited for generic object recognition. However, it requires storing the complete training data, and classification time is linear in ...
Ferid Bajramovic, Frank Mattern, Nicholas Butko, J...
Abstract. We introduce a nonparametric model for sensitivity estimation which relies on generating points similar to the prediction point using its k nearest neighbors. Unlike most...
This work investigates a geometric approach to proving cell probe lower bounds for data structure problems. We consider the approximate nearest neighbor search problem on the Bool...
In this paper, we propose a novel method for fast nearest neighbors retrieval in non-Euclidean and non-metric spaces. We organize the data into a hierarchical fashion that preserv...
—We present an overview of the combinatorial framework for similarity search. An algorithm is combinatorial if only direct comparisons between two pairwise similarity values are ...
This paper shows that using some very simple practical assumptions, one can design an algorithm that finds the nearest neighbor of a given query point in O(log n) time in theory ...