The nearest neighbor classification/regression technique, besides its simplicity, is one of the most widely applied and well studied techniques for pattern recognition in machine learning. A nearest neighbor classifier assumes class conditional probabilities to be locally smooth. This assumption is often invalid in high dimensions and significant bias can be introduced when using the nearest ne...

The vast number of applications featuring multimedia and geometric data has made the R-tree a ubiquitous data structure in databases. A popular and fundamental operation on R-trees is nearest neighbor search. While nearest neighbor on R-trees has received considerable experimental attention, it has received somewhat less theoretical consideration. We study pruning heuristics for nearest neighbo...

Multi-class image segmentation is a complex problem that poses several challenges: developing better classifiers, designing more discriminative features, finding efficient optimization techniques and modeling the relations between image pixels in different image regions. In this paper we focus on the last one. A common way to address the problem of structured prediction is to model it as a Cond...

As hospitals are increasingly held accountable for patients' post-discharge outcomes under new payment models, hospitals may choose to acquire skilled nursing facilities (SNFs) to better manage these outcomes. This raises the question of whether patients discharged to hospital-based SNFs have better outcomes. In unadjusted comparisons, hospital-based SNF patients have much lower Medicare utiliz...

This paper proposes a spectral clustering method using k-means and weighted Mahalanobis distance (Referred to as MDLSC) enhance the degree of correlation between data points improve accuracy Laplacian matrix eigenvectors. First, we used coefficient weight calculate any two constructed set; then, based on matrix, K-nearest neighborhood (KNN) algorithm construct similarity matrix. Secondly, regul...

We suggest a simple modification to the kd-tree search algorithm for nearest neighbor search resulting in an improved performance. The Kd-tree data structure seems to work well in finding nearest neighbors in low dimensions but its performance degrades even if the number of dimensions increases to more than three. Since the exact nearest neighbor search problem suffers from the curse of dimensi...

We suggest a simple modification to the Kd-tree search algorithm for nearest neighbor search resulting in an improved performance. The Kd-tree data structure seems to work well in finding nearest neighbors in low dimensions but its performance degrades even if the number of dimensions increases to more than two. Since the exact nearest neighbor search problem suffers from the curse of dimension...

This paper presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure is obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase. For those data bases in which s...

0167-8655/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.patrec.2010.11.009 ⇑ Corresponding author. Tel./fax: +86 029 8266877 E-mail address: [email protected] (D.-Q. Han). A new approach called shortest feature line segment (SFLS) is proposed to implement pattern classification in this paper, which can retain the ideas and advantages of nearest feature line (NFL) and at the same time can...

Let D n × denote the distance matrix of objects, and let T be an unrooted binary tree in which leaves those objects. We want to find such a with constraint that edge weights are nonnegative where distances between best estimate their corresponding values D. Accordingly, we have adopted residual sum squares (RSS) criterion minimize discrepancy For this optimization problem, designed iterated loc...