in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subset-subset matching. A least-squares technique is used to estimate 3-D motion from the point

This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because

(schemas, catalogs, or other data structures) as input, and produces as output a mapping between corresponding nodes of the graphs. Depending on the matching goal, a subset of the mapping is chosen using filters. After our algorithm runs, we expect a human to check and if necessary adjust the results. As a

is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC

State of the art, real-time, rate-adaptive, multimedia applications adjust their transmission rate to match the available network capacity. Unfortunately, this source-based rate-adaptation performs poorly in a heterogeneous multicast environment because there is no single target rate

technique for clustering these large, high-dimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common

are all different. In this paper, a new filtering algorithm for these constraints is presented. It achieves the generalized arc-consistency condition for these non-binary constraints. It is based on matching theory and its complexity is low. In fact, for a constraint defined on a subset of p variables

, the matching problem for content-based systems --- determining for each event the subset of all subscriptions whose predicates match the event --- is still an open problem. We present an efficient, scalable solution to the matching problem. Our solution has an expected time complexity that is sub

This paper is the first of two papers describing an O (n polylog(m)) time algorithm for the Tree Pattern Matching problem on a pattern of size m and a text of size n. In this paper, we show an O(n+m) time Turing reduction from the Tree Pattern Matching problem to another problem called the Subset

This paper describes an O(s log³ s) time deterministic algorithm, an O(s ) randomized Las Vegas algorithm, and an O(s log s) time randomized Monte Carlo algorithm for the Subset Matching problem. Here, s is the sum of the sizes of the text and pattern sets. A variant of