Results 1  10
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1,137
Loopy belief propagation for approximate inference: An empirical study. In:
 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 errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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. Introduction The task of calculating posterior marginals on nodes in an arbitrary Bayesian network is known to be NP hard In this paper we investigate the approximation performance of "loopy belief propagation". This refers to using the wellknown Pearl polytree algorithm [12] on a Bayesian network
The R*tree: an efficient and robust access method for points and rectangles
 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
, 1990
"... The Rtree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design the ..."
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Cited by 1262 (74 self)
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The Rtree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design
Large margin dags for multiclass classification
 Advances in Neural Information Processing Systems 12
, 2000
"... We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many twoclass classifiers into a multiclass classifier. For anclass problem, the DDAG contains � classifiers, one for each pair of classes. We present a VC analysis of the case when the nod ..."
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Cited by 374 (1 self)
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the node classifiers are hyperplanes; the resulting bound on the test error depends on and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernelinduced feature space and uses twoclass maximal margin hyperplanes at each
Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
 Machine Learning
, 1997
"... We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomplete data given a Bayesian network. We consider the Laplace approximation and the less accurate but more efficient BIC/MD ..."
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Cited by 194 (12 self)
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We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomplete data given a Bayesian network. We consider the Laplace approximation and the less accurate but more efficient BIC
On the Marginal Utility of Network Topology Measurements
, 2001
"... The cost and complexity of deploying measurement infrastructure in the Internet for the purpose of analyzing its structure and behavior is considerable. Basic questions about the utility of increasing the number of measurements and measurement sites have not yet been addressed which has led to a &qu ..."
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Cited by 115 (12 self)
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"more is better" approach to widearea measurement studies. In this paper, we step toward a more quantifiable understanding of the marginal utility of performing widearea measurements in the context of Internet topology discovery. We characterize the observable topology in terms of nodes
Generic FactorBased Node Marginalization and Edge Sparsification for PoseGraph SLAM
"... Abstract—This paper reports on a factorbased method for node marginalization in simultaneous localization and mapping (SLAM) posegraphs. Node marginalization in a posegraph induces fillin and leads to computational challenges in performing inference. The proposed method is able to produce a new ..."
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Cited by 11 (6 self)
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Abstract—This paper reports on a factorbased method for node marginalization in simultaneous localization and mapping (SLAM) posegraphs. Node marginalization in a posegraph induces fillin and leads to computational challenges in performing inference. The proposed method is able to produce a new
Boosting margin based distance functions for clustering
 Proc. of ICML
, 2004
"... The performance of graph based clustering methods critically depends on the quality of the distance function, used to compute similarities between pairs of neighboring nodes. In this paper we learn distance functions by training binary classifiers with margins. The classifiers are defined over the p ..."
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Cited by 47 (6 self)
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The performance of graph based clustering methods critically depends on the quality of the distance function, used to compute similarities between pairs of neighboring nodes. In this paper we learn distance functions by training binary classifiers with margins. The classifiers are defined over
On the Marginal Utility of Deploying Measurement Infrastructure
, 2000
"... this paper, we present a more re ned and quanti able understanding of the marginal utility of performing widearea measurements. We focus on problems in Internet topology discovery, namely, discovering the set of nodes and links which comprise the Internet backbone, discovering the degree distr ..."
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Cited by 16 (2 self)
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this paper, we present a more re ned and quanti able understanding of the marginal utility of performing widearea measurements. We focus on problems in Internet topology discovery, namely, discovering the set of nodes and links which comprise the Internet backbone, discovering the degree
Enlarging the Margins in Perceptron Decision Trees
, 2000
"... Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting. In particular, we provide an upper bound on the generalization error which depends both on the size of ..."
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Cited by 26 (3 self)
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of the tree and on the margin of the decision nodes. So enlarging the margin in perceptron decision trees will reduce the upper bound on generalization error. Based on this analysis, we introduce three new algorithms, which can induce large margin perceptron decision trees. To assess the effect of the large
Marginal AMP Chain Graphs
 INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
, 2014
"... We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them is Markov equivalent to some AMP chain graph under marginalization of some of its nodes. However, MAMP c ..."
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Cited by 2 (2 self)
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We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them is Markov equivalent to some AMP chain graph under marginalization of some of its nodes. However, MAMP
Results 1  10
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