The condensed nearest neighbor (CNN) algorithm is a heuristic for reducing the number of prototypical points stored by a nearest neighbor classifier, while keeping the classification rule given by the reduced prototypical set consistent with the full set. I present an upper bound on the number of prototypical points accumulated by CNN. The bound originates in a bound on the number of times the ...

In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that...

In recent years, researchers started to study the game of Skat. The strength of existing Skat playing programs is definitely the card play phase. The bidding phase, however, has been treated quite poorly so far. This is a severe drawback since bidding abilities influence the overall playing performance drastically. In this paper we present a powerful bidding engine which is based on a k-nearest...

We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competitive with those obtained by previously proposed passive learners. We prove th...

If we pick n random points uniformly in [0, 1]d and connect each point to its k−nearest neighbors, then it is well known that there exists a giant connected component with high probability. We prove that in [0, 1]d it suffices to connect every point to cd,1 log logn points chosen randomly among its cd,2 logn−nearest neighbors to ensure a giant component of size n− o(n) with high probability. Th...

This study examines global patterns of stability and change within six longitudinal samples of English-language weblogs (or “blogs”) during the 2004 U.S. Presidential election campaign. Using distance-based methods of graph comparison, we explore the evolution of the blog-blog citation networks for each sample during the period. In addition to describing the qualitative dynamics of the blog net...

In this work we present a technique to obtain prediction intervals for chaotic data. Using nearest neighbors method we give estimates of local variance and percentiles of the prediction error distribution. This allows to define an interval containing a future value with a given probability. Its effectiveness is shown with data generated by a chaotic economic model.  2001 Elsevier Science B.V. ...

Since most classi%cation articles have applied a single technique to a single gene expression dataset, it is crucial to assess the performance of each method through a comprehensive comparative study. We evaluate by extensive comparison study extending Dudoit et al. (J. Amer. Statist. Assoc. 97 (2002) 77) the performance of recently developed classi%cation methods in microarray experiment, and ...

The patents cover almost all the latest, the most active innovative technical information in technical fields, therefore patent classification has great application value in the patent research domain. This paper presents a KNN text categorization method based on shared nearest neighbor, effectively combining the BM25 similarity calculation method and the Neighborhood Information of samples. Th...

We describe the FlexTrack system for querying trajectories using flexible pattern queries. Such queries are composed of a sequence of simple spatio-temporal predicates, e.g., range and nearest-neighbors, as well as complex motion pattern predicates, e.g., predicates that contain variables and constraints. Users can interactively select spatio-temporal predicates to construct such pattern querie...