Results 21 to 30 of about 170,592 (187)

Functional Dependence and Analytic Functions [PDF]

open access: yesCanadian Mathematical Bulletin, 1979
AbstractWithout appealing to the Cauchy theorem or its corollaries, it is proved that the real and imaginary parts of a non-constant complex-valued analytic function of several complex variables are functionally independent. This unifies and generalizes some results sporadically treated in standard treatises on function theory.
openaire   +2 more sources

Wide-coverage deep statistical parsing using automatic dependency structure annotation [PDF]

open access: yes, 2008
A number of researchers (Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004;Miyao and Tsujii 2004) have convincingly argued for the use of dependency (rather
O'Donovan, Ruth   +6 more
core   +1 more source

Functional Dependencies with null Markers [PDF]

open access: yesThe Computer Journal, 2014
Functional dependencies are an integral part of database design. However, they are only defined when we exclude null markers. Yet we commonly use null markers in practice. To bridge this gap between theory and practice, researchers have proposed definitions of functional dependencies over relations with null markers.
Antonio Badia, Daniel Lemire
openaire   +3 more sources

Dependent Functional Data [PDF]

open access: yesISRN Probability and Statistics, 2012
This paper reviews recent research on dependent functional data. After providing an introduction to functional data analysis, we focus on two types of dependent functional data structures: time series of curves and spatially distributed curves. We review statistical models, inferential methodology, and possible extensions.
openaire   +1 more source

Dependency-based automatic evaluation for machine translation [PDF]

open access: yes, 2007
We present a novel method for evaluating the output of Machine Translation (MT), based on comparing the dependency structures of the translation and reference rather than their surface string forms.
Andy Way   +5 more
core   +1 more source

How to Mine Information from Each Instance to Extract an Abbreviated and Credible Logical Rule

open access: yesEntropy, 2014
Decision trees are particularly promising in symbolic representation and reasoning due to their comprehensible nature, which resembles the hierarchical process of human decision making.
Limin Wang, Minghui Sun, Chunhong Cao
doaj   +1 more source

Bounds for Functions of Dependent Risks [PDF]

open access: yesFinance and Stochastics, 2006
For an \(n\)-variate real function \(\psi\) and a random vector \(X:=(X_1,\dots,X_n)\) the problem of finding the best possible lower bound on the distribution function of \(\psi(X)\) is studied when the marginal distributions of the individual risks \(X_i\) are given and the structure of dependence of \(X\) is partially or completely unknown.
Paul Embrechts, Giovanni Puccetti 0001
openaire   +1 more source

Dependency-based n-gram models for general purpose sentence realisation [PDF]

open access: yes, 2008
We present dependency-based n-gram models for general-purpose, widecoverage, probabilistic sentence realisation. Our method linearises unordered dependencies in input representations directly rather than via the application of grammar rules, as in ...
Guo, Yuqing   +5 more
core   +1 more source

Discovering Graph Functional Dependencies [PDF]

open access: yesACM Transactions on Database Systems, 2018
This article studies discovery of Graph Functional Dependencies (GFDs), a class of functional dependencies defined on graphs. We investigate the fixed-parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed-parameter tractable, but the validation problem is co-W[1]
Wenfei Fan   +3 more
openaire   +2 more sources

Mining Conditional Functional Dependency Rules on Big Data

open access: yesBig Data Mining and Analytics, 2020
Current Conditional Functional Dependency (CFD) discovery algorithms always need a well-prepared training dataset. This condition makes them difficult to apply on large and low-quality datasets.
Mingda Li, Hongzhi Wang, Jianzhong Li
doaj   +1 more source

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