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Learning Bayesian Networks With Local Structure

by Nir Friedman, Moises Goldszmidt , 1996
"... . We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability distributions (CPDs) that quantify these networks. This inc ..."
Abstract - Cited by 272 (12 self) - Add to MetaCart
. We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability distributions (CPDs) that quantify these networks

Hierarchical Bayesian Optimization Algorithm = Bayesian Optimization Algorithm + Niching + Local Structures

by Martin Pelikan, David. E. Goldberg , 2001
"... The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently. ..."
Abstract - Cited by 329 (70 self) - Add to MetaCart
The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently.

Incorporating non-local information into information extraction systems by Gibbs sampling

by Jenny Rose Finkel, Trond Grenager, Christopher Manning - IN ACL , 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
Abstract - Cited by 730 (25 self) - Add to MetaCart
Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling

Locally weighted learning

by Christopher G. Atkeson, Andrew W. Moore , Stefan Schaal - ARTIFICIAL INTELLIGENCE REVIEW , 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract - Cited by 599 (51 self) - Add to MetaCart
This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias

Learning with local and global consistency.

by Dengyong Zhou , Olivier Bousquet , Thomas Navin Lal , Jason Weston , Bernhard Schölkopf - In NIPS, , 2003
"... Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intr ..."
Abstract - Cited by 673 (21 self) - Add to MetaCart
to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

Query Expansion Using Local and Global Document Analysis

by Jinxi Xu, W. Bruce Croft - In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 1996
"... Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention has focused on techniques that analyze the corpus to discover word re ..."
Abstract - Cited by 610 (24 self) - Add to MetaCart
global analysis techniques, such as word context and phrase structure, on the local set of documents produces results that are both more effective and more predictable than simple local feedback. 1 Introduction The problem of word mismatch is fundamental to information retrieval. Simply stated, it means

Locality-sensitive hashing scheme based on p-stable distributions

by Mayur Datar, Piotr Indyk - In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry , 2004
"... inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
Abstract - Cited by 521 (8 self) - Add to MetaCart
inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate

A solution to the simultaneous localization and map building (SLAM) problem

by M. W. M. Gamini Dissanayake, Paul Newman, Steven Clark, Hugh F. Durrant-whyte, M. Csorba - IEEE Transactions on Robotics and Automation , 2001
"... Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle ..."
Abstract - Cited by 505 (30 self) - Add to MetaCart
Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle

Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features

by Wolfgang Kabsch, Christian Sander , 1983
"... For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-r ..."
Abstract - Cited by 2096 (5 self) - Add to MetaCart
. ” Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain “chirality ” is the torsional handedness of four consecutive Ca positions and is positive for right-handed helices and negative for ideal twisted @-sheets. Curved pieces are defined as “bends

Global Spontaneous Activity and Local Structured (learned) Delay Activity in Cortex

by Daniel J. Amit, La Sapienza, Ple Aldo Moro, Nicolas Brunel, Universita Di Roma, Universita Di Roma , 1996
"... to any of the stimuli learned have rates which gradually increase with the amplitude of synaptic potentiation. b. When the average LTP increases beyond a critical value, specific local attractors appear abruptly against the background of the global uniform spontaneous attractor. This happens with e ..."
Abstract - Cited by 306 (26 self) - Add to MetaCart
to any of the stimuli learned have rates which gradually increase with the amplitude of synaptic potentiation. b. When the average LTP increases beyond a critical value, specific local attractors appear abruptly against the background of the global uniform spontaneous attractor. This happens
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