This note makes two comments on recent NNS models. First, it disputes the way physical capital has been introduced into these models arguing that this leads to the dubious postulate that the cost of adjusting physical capital stock is an order of magnitude lower than the cost of changing prices. Second it warns against a possible logical inconsistency whereby calibrated NNS models are implicitl...

In this paper two classifiers, which generalize the nearest neighbor method, are introduced and studied. The first of them is based on

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 and faster than the state of the art in practice. The algorithm and proof are both simple and the experimental results clearly show that we can beat the state of the art on most distributions in two dimensions.

Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision. The performance of traditional hierarchical structure (tree) based methods decreases as the dimensionality of data grows, while hashing based methods usually lack efficiency in practice. Recently, the graph based methods have drawn considerable attention. The ma...

Spatial diffusion processes can be seen in many geographic phenomena that spread or migrate across space and over time. Studies of these processes were mostly done with verbal description until Hägerstrand (1966) started to approach it with quantitative models. A variety of attempts were made to continue this effort, but only with various degrees of success. Recognizing the critical role that d...

We develop a data structure for answering efficiently approximate nearest neighbor queries over a set of parallel segments in three dimensions. We connect this problem to approximate nearest neighbor searching under weight constraints and approximate nearest neighbor searching on historical data in any dimension and give efficient solutions for these as well.

Multi-view classification is a machine learning methodology when patterns or objects of interest are represented by a set of different views (sets of features) rather than the union of all views. In this paper, multiple views are employed in ensembles of nearest neighbor classifiers where they demonstrate promising results in classifying a challenging data set of protein folds. In particular, u...

Nearest neighbor (NN) classification assumes locally constant class conditional probabilities, and suffers from bias in high dimensions with a small sample set. In this paper, we propose a novel cam weighted distance to ameliorate the curse of dimensionality. Different from the existing neighborhood-based methods which only analyze a small space emanating from the query sample, the proposed nea...

Drought is a climate phenomenon which might occur in any climate condition and all regions on the earth. Effective drought management depends on the application of appropriate drought indices. Drought indices are variables which are used to detect and characterize drought conditions. In this study, it was tried to predict drought occurrence, based on the standard precipitation index (SPI), usin...