Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
179,493
Learning probabilistic relational models
- In IJCAI
, 1999
"... A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
Abstract
-
Cited by 613 (30 self)
- Add to MetaCart
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much
Probabilistic Inference Using Markov Chain Monte Carlo Methods
, 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
Abstract
-
Cited by 736 (24 self)
- Add to MetaCart
. Related problems in other fields have been tackled using Monte Carlo methods based on sampling using Markov chains, providing a rich array of techniques that can be applied to problems in artificial intelligence. The "Metropolis algorithm" has been used to solve difficult problems in statistical
Learning to predict by the methods of temporal differences
- MACHINE LEARNING
, 1988
"... This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predi ..."
Abstract
-
Cited by 1521 (56 self)
- Add to MetaCart
, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce
Accurate Methods for the Statistics of Surprise and Coincidence
- COMPUTATIONAL LINGUISTICS
, 1993
"... Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used unjustifiably ..."
Abstract
-
Cited by 1057 (1 self)
- Add to MetaCart
unjustifiably, leading to flawed results.This assumption of normal distribution limits the ability to analyze rare events. Unfortunately rare events do make up a large fraction of real text.However, more applicable methods based on likelihood ratio tests are available that yield good results with relatively
Symmetry and Related Properties via the Maximum Principle
, 1979
"... We prove symmetry, and some related properties, of positive solutions of second order elliptic equations. Our methods employ various forms of the maximum principle, and a device of moving parallel planes to a critical position, and then showing that the solution is symmetric about the limiting plan ..."
Abstract
-
Cited by 538 (4 self)
- Add to MetaCart
We prove symmetry, and some related properties, of positive solutions of second order elliptic equations. Our methods employ various forms of the maximum principle, and a device of moving parallel planes to a critical position, and then showing that the solution is symmetric about the limiting
Analysis of relative gene expression data using real-time quantitative
- PCR and 2 ���CT method. Methods 25
, 2001
"... of the target gene relative to some reference group The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantifica-such as an untreated control or a sample at time zero tion and relative quantification. Absolute quantification deter- in a time ..."
Abstract
-
Cited by 2666 (6 self)
- Add to MetaCart
of the target gene relative to some reference group The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantifica-such as an untreated control or a sample at time zero tion and relative quantification. Absolute quantification deter- in a
Boosting the margin: A new explanation for the effectiveness of voting methods
- IN PROCEEDINGS INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1997
"... One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this ..."
Abstract
-
Cited by 897 (52 self)
- Add to MetaCart
that techniques used in the analysis of Vapnik’s support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins
A Bayesian method for the induction of probabilistic networks from data
- MACHINE LEARNING
, 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
Abstract
-
Cited by 1400 (31 self)
- Add to MetaCart
network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
A comparison of bayesian methods for haplotype reconstruction from population genotype data.
- Am J Hum Genet
, 2003
"... In this report, we compare and contrast three previously published Bayesian methods for inferring haplotypes from genotype data in a population sample. We review the methods, emphasizing the differences between them in terms of both the models ("priors") they use and the computational str ..."
Abstract
-
Cited by 557 (7 self)
- Add to MetaCart
operates through the transmission of chromosomal segments. Experimental methods for haplotype determination exist, but they are currently timeconsuming and expensive. Statistical methods for inferring haplotypes are therefore of considerable interest. In some studies, data may be available on related
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
- JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 1999
"... Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives ..."
Abstract
-
Cited by 515 (4 self)
- Add to MetaCart
and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDP-related
Results 1 - 10
of
179,493
