Results 1 - 10 of 3,175

Randomized Gossip Algorithms

by Stephen Boyd, Arpita Ghosh, Balaji Prabhakar, Devavrat Shah - IEEE TRANSACTIONS ON INFORMATION THEORY , 2006
"... Motivated by applications to sensor, peer-to-peer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
Abstract - Cited by 532 (5 self) - Add to MetaCart
distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for an arbitrary network graph, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly

Random Key Predistribution Schemes for Sensor Networks”,

by Haowen Chan , Adrian Perrig , Dawn Song - IEEE Symposium on Security and Privacy, , 2003
"... Abstract Efficient key distribution is the basis for providing secure communication, a necessary requirement for many emerging sensor network applications. Many applications require authentic and secret communication among neighboring sensor nodes. However, establishing keys for secure communicatio ..."
Abstract - Cited by 832 (12 self) - Add to MetaCart
before deployment. In the field, neighboring nodes exchange information to find one common key within their random subset and use that key as their shared secret to secure subsequent communication. In this paper, we generalize the Eschenauer-Gligor key distribution approach. First, we propose two new

Fast approximate nearest neighbors with automatic algorithm configuration

by Marius Muja, David G. Lowe - In VISAPP International Conference on Computer Vision Theory and Applications , 2009
"... nearest-neighbors search, randomized kd-trees, hierarchical k-means tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems ..."
Abstract - Cited by 455 (2 self) - Add to MetaCart
nearest-neighbors search, randomized kd-trees, hierarchical k-means tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

by Yongyue Zhang, Michael Brady, Stephen Smith - IEEE TRANSACTIONS ON MEDICAL. IMAGING , 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
Abstract - Cited by 639 (15 self) - Add to MetaCart
-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown

The capacity of wireless networks

by Piyush Gupta, P. R. Kumar - IEEE TRANSACTIONS ON INFORMATION THEORY , 2000
"... When n identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput @ A obtainable by each node for a randomly chosen destination is 2 bits per second under a noninterference protocol. If the nodes are optimally p ..."
Abstract - Cited by 3243 (42 self) - Add to MetaCart
When n identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput @ A obtainable by each node for a randomly chosen destination is 2 bits per second under a noninterference protocol. If the nodes are optimally

Experiments with a New Boosting Algorithm

by Yoav Freund, Robert E. Schapire , 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
Abstract - Cited by 2213 (20 self) - Add to MetaCart
In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced

Loopy belief propagation for approximate inference: An empirical study. In:

by Kevin P Murphy , Yair Weiss , Michael I Jordan - Proceedings of Uncertainty in AI, , 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract - Cited by 676 (15 self) - Add to MetaCart
modification to the update rules in that we normalized both ..\ and 1r messages at each iteration. As Pearl Nodes were updated in parallel: at each iteration all nodes calculated their outgoing messages based on the incoming messages of their neighbors from the pre vious iteration. The messages were said

Routing indices for peer-to-peer systems

by Arturo Crespo, Hector Garcia-molina , 2002
"... Finding information in a peer-to-peer system currently requires either a costly and vulnerable central index, or ooding the network with queries. In this paper we introduce the concept of Routing Indices (RIs), which allow nodes to forward queries to neighbors that are more likely to have answers. I ..."
Abstract - Cited by 423 (15 self) - Add to MetaCart
. If a node cannot answer a query, it forwards the query to a subset of its neighbors, based on its local RI, rather than by selecting neighbors at random or by ooding the network by forwarding the query to all neighbors. We present three RI schemes: the compound, the hop-count, and the exponential

Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks

by Chris Savarese, Jan Rabaey, Koen Langendoen , 2002
"... A distributed algorithm for determining the positions of nodes in an ad-hoc, wireless sensor network is explained in detail. Details regarding the implementation of such an algorithm are also discussed. Experimentation is performed on networks containing 400 nodes randomly placed within a square are ..."
Abstract - Cited by 383 (9 self) - Add to MetaCart
A distributed algorithm for determining the positions of nodes in an ad-hoc, wireless sensor network is explained in detail. Details regarding the implementation of such an algorithm are also discussed. Experimentation is performed on networks containing 400 nodes randomly placed within a square

Eigentaste: A Constant Time Collaborative Filtering Algorithm

by Ken Goldberg, Theresa Roeder, Dhruv Gupta, Chris Perkins , 2000
"... Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clusterin ..."
Abstract - Cited by 378 (6 self) - Add to MetaCart
clustering of users and rapid computation of recommendations. For a database of n users, standard nearest-neighbor techniques require O(n) processing time to compute recommendations, whereas Eigentaste requires O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from Jester
Results 1 - 10 of 3,175