Results 1  10
of
10,512
Hierarchical Problem Generator (HPG). Hierarchical problems
"... We describe a generator for hierarchical problems called the ..."
Search Reduction in Hierarchical Problem Solving
, 1991
"... It has long been recognized that hierarchical problem solving can be used to reduce search. Yet, there has been little analysis of the problemsolving method and few experimental results. This paper provides the first comprehensive analytical and empirical demonstrations of the effectiveness of ..."
Abstract

Cited by 68 (1 self)
 Add to MetaCart
It has long been recognized that hierarchical problem solving can be used to reduce search. Yet, there has been little analysis of the problemsolving method and few experimental results. This paper provides the first comprehensive analytical and empirical demonstrations of the effectiveness
An Iterative Algorithm for a Hierarchical Problem
"... A general hierarchical problem has been considered, and an explicit algorithm has been presented for solving this hierarchical problem. Also, it is shown that the suggested algorithm converges strongly to a solution of the hierarchical problem. ..."
Abstract
 Add to MetaCart
A general hierarchical problem has been considered, and an explicit algorithm has been presented for solving this hierarchical problem. Also, it is shown that the suggested algorithm converges strongly to a solution of the hierarchical problem.
Hierarchical Dirichlet processes.
 Journal of the American Statistical Association,
, 2006
"... We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this s ..."
Abstract

Cited by 942 (78 self)
 Add to MetaCart
Carlo algorithms for posterior inference in hierarchical Dirichlet process mixtures and describe applications to problems in information retrieval and text modeling.
Hierarchical mixtures of experts and the EM algorithm
, 1993
"... We present a treestructured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a maximum likelihood ..."
Abstract

Cited by 885 (21 self)
 Add to MetaCart
We present a treestructured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a maximum likelihood
Hierarchically Classifying Documents Using Very Few Words
, 1997
"... The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. Existing classification schemes which ignore the hierarchical structure and treat the topics as separate classes are often inadequate in text ..."
Abstract

Cited by 521 (8 self)
 Add to MetaCart
classification where the there is a large number of classes and a huge number of relevant features needed to distinguish between them. We propose an approach that utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification
ABSTRACT On the Complexity of Hierarchical Problem Solving
"... Competent Genetic Algorithms can efficiently address problems in which the linkage between variables is limited to a small order k. Problems with higher order dependencies can only be addressed efficiently if further problem properties exist that can be exploited. An important class of problems for ..."
Abstract
 Add to MetaCart
for which this occurs is that of hierarchical problems. Hierarchical problems can contain dependencies between all variables (k = n) while being solvable in polynomial time. An open question so far is what precise properties a hierarchical problem must possess in order to be solvable efficiently. We study
Imagenet: A largescale hierarchical image database
 In CVPR
, 2009
"... The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce her ..."
Abstract

Cited by 840 (28 self)
 Add to MetaCart
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce
Downward Refinement and the Efficiency of Hierarchical Problem Solving
 Artificial Intelligence
, 1993
"... Analysis and experiments have shown that hierarchical problemsolving is most effective when the hierarchy satisfies the downward refinement property (DRP), whereby every abstract solution can be refined to a concretelevel solution without backtracking across abstraction levels. However, the DRP i ..."
Abstract

Cited by 63 (1 self)
 Add to MetaCart
Analysis and experiments have shown that hierarchical problemsolving is most effective when the hierarchy satisfies the downward refinement property (DRP), whereby every abstract solution can be refined to a concretelevel solution without backtracking across abstraction levels. However, the DRP
Prior distributions for variance parameters in hierarchical models
 Bayesian Analysis
, 2006
"... Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new foldednoncentralt family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors i ..."
Abstract

Cited by 430 (15 self)
 Add to MetaCart
in this family. We use an example to illustrate serious problems with the inversegamma family of “noninformative ” prior distributions. We suggest instead to use a uniform prior on the hierarchical standard deviation, using the halft family when the number of groups is small and in other settings where a
Results 1  10
of
10,512