Nearest neighbor (NN) methods include at least six different groups of statistical methods. All have in common the idea that some aspect of the similarity between a point and its NN can be used to make useful inferences. In some cases, the similarity is the distance between the point and its NN; in others, the appropriate similarity is based on other identifying characteristics of the points. I...

Preprocess: a set D of points in R d Query: given a new point q, report a point pD with the smallest distance to q q p Motivation

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

This paper presents a new approach for solving binary quantification problems based on nearest neighbor (NN) algorithms. Our main objective is to study the behavior of these methods in the context of prevalence estimation. We seek for NN-based quantifiers able to provide competitive performance while balancing simplicity and effectiveness. We propose two simple weighting strategies, PWK and PWK...

The nearest-neighbor based document skew detection methods do not require the presence of a predominant text area, and are not subject to skew angle limitation. However, the accuracy of these methods is not perfect in general. In this paper, we present an improved nearest-neighbor based approach to perform accurate document skew estimation. Size restriction is introduced to the detection of nea...