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Approximation by Superpositions of a Sigmoidal Function
, 1989
"... In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set ofaffine functionals can uniformly approximate any continuous function of n real variables with support in the unit hypercube; only mild conditions are imposed on the univariate fun ..."
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Cited by 1248 (2 self)
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function. Our results settle an open question about representability in the class of single bidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well approximated by continuous feedforward neural networks with only a single internal, hidden layer and any
The knowledge complexity of interactive proof systems

, 1989
"... Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/nonHamiltonian. In th ..."
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Cited by 1246 (39 self)
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Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian
Bayesian Network Classifiers
, 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
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Cited by 796 (20 self)
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Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less
Stacked generalization
 NEURAL NETWORKS
, 1992
"... This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second sp ..."
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Cited by 731 (9 self)
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sophisticated version of crossvalidation, exploiting a strategy more sophisticated than crossvalidation’s crude winnertakesall for combining the individual generalizers. When used with a single generalizer, stacked generalization is a scheme for estimating (and then correcting for) the error of a
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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with loops (undirected cycles). The algorithm is an exact inference algorithm for singly connected networks the beliefs converge to the cor rect marginals in a number of iterations equal to the diameter of the graph.1 However, as Pearl noted, the same algorithm will not give the correct beliefs for mul
Genomic control for association studies
, 1999
"... A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. ..."
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Cited by 480 (13 self)
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A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders
BRITE: An approach to universal topology generation,”
 in Proceedings of the IEEE Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems,
, 2001
"... Abstract Effective engineering of the Internet is predicated upon a detailed understanding of issues such as the largescale structure of its underlying physical topology, the manner in which it evolves over time, and the way in which its constituent components contribute to its overall function. U ..."
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Cited by 448 (12 self)
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of topology models, it is an open question as to how representative the generated topologies they generate are of the actual Internet. Our goal is to produce a topology generation framework which improves the state of the art and is based on the design principles of representativeness, inclusiveness
Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments
 STATISTICA SINICA
, 2002
"... DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. A ..."
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Cited by 438 (12 self)
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. Although it is not the main focus of the paper, new methods for the important preprocessing steps of image analysis and normalization are proposed. Given suitably normalized data, the biological question of differential expression is restated as a problem in multiple hypothesis testing: the simultaneous
Statistical analysis in Climate Research
, 1999
"... Climate is a complex system: it has many variables, and they are acting nonlinearly, in general. Therefore, no exact answers to questions should be expected, and many climatic processes are and will be poorly understood. That means that statistical analysis is undeniable in climate research. This si ..."
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Cited by 383 (9 self)
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Climate is a complex system: it has many variables, and they are acting nonlinearly, in general. Therefore, no exact answers to questions should be expected, and many climatic processes are and will be poorly understood. That means that statistical analysis is undeniable in climate research
Rapid worldwide depletion of predatory fish communities.
 Nature,
, 2003
"... Serious concerns have been raised about the ecological effects of industrialized fishing Ecological communities on continental shelves and in the open ocean contribute almost half of the planet's primary production 9 , and sustain threequarters of global fishery yields 1 . The widespread dec ..."
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Cited by 367 (7 self)
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in coastal ecosystem structure and function 5 . Such baseline information is scarce for shelf and oceanic ecosystems. Although there is an understanding of the magnitude of the decline in single stocks 10 , it is an open question how entire communities have responded to
Results 1  10
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