Results 1 - 10 of 84,598

Generalized Autoregressive Conditional Heteroskedasticity

by Tim Bollerslev - JOURNAL OF ECONOMETRICS , 1986
"... A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametri ..."
Abstract - Cited by 2406 (30 self) - Add to MetaCart
A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class

An algorithm for drawing general undirected graphs

by Tomihisa Kamada, Satoru Kawai - Information Processing Letters , 1989
"... Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets, and entit ..."
Abstract - Cited by 698 (2 self) - Add to MetaCart
Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets

Longitudinal data analysis using generalized linear models”.

by Kung-Yee Liang , Scott L Zeger - Biometrika, , 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
Abstract - Cited by 1526 (8 self) - Add to MetaCart
SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence

Primitives for the manipulation of general subdivisions and the computations of Voronoi diagrams

by Leonidas Guibas, Jorge Stolfi - ACM Tmns. Graph , 1985
"... The following problem is discussed: Given n points in the plane (the sites) and an arbitrary query point 4, find the site that is closest to q. This problem can be solved by constructing the Voronoi diagram of the given sites and then locating the query point in one of its regions. Two algorithms ar ..."
Abstract - Cited by 534 (11 self) - Add to MetaCart
to the separation of the geometrical and topological aspects of the problem and to the use of two simple but powerful primitives, a geometric predicate and an operator for manipulating the topology of the diagram. The topology is represented by a new data structure for generalized diagrams, that is, embeddings

Convolution Kernels on Discrete Structures

by David Haussler , 1999
"... We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes the fa ..."
Abstract - Cited by 506 (0 self) - Add to MetaCart
We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on an infinite set from kernels involving generators of the set. The family of kernels generated generalizes

An equilibrium characterization of the term structure.

by Oldrich Vasicek - J. Financial Econometrics , 1977
"... The paper derives a general form of the term structure of interest rates. The following assumptions are made: (A.l) The instantaneous (spot) interest rate follows a diffusion process; (A.2) the price of a discount bond depends only on the spot rate over its term; and (A.3) the market is efficient. ..."
Abstract - Cited by 1041 (0 self) - Add to MetaCart
The paper derives a general form of the term structure of interest rates. The following assumptions are made: (A.l) The instantaneous (spot) interest rate follows a diffusion process; (A.2) the price of a discount bond depends only on the spot rate over its term; and (A.3) the market is efficient

Finding structure in time

by Jeffrey L. Elman - COGNITIVE SCIENCE , 1990
"... Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a pro ..."
Abstract - Cited by 2071 (23 self) - Add to MetaCart
; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method

Improved algorithms for optimal winner determination in combinatorial auctions and generalizations

by Tuomas Sandholm, Subhash Suri , 2000
"... Combinatorial auctions can be used to reach efficient resource and task allocations in multiagent systems where the items are complementary. Determining the winners is NP-complete and inapproximable, but it was recently shown that optimal search algorithms do very well on average. This paper present ..."
Abstract - Cited by 582 (53 self) - Add to MetaCart
presents a more sophisticated search algorithm for optimal (and anytime) winner determination, including structural improvements that reduce search tree size, faster data structures, and optimizations at search nodes based on driving toward, identifying and solving tractable special cases. We also uncover

Optimally sparse representation in general (non-orthogonal) dictionaries via ℓ¹ minimization

by David L. Donoho, Michael Elad - PROC. NATL ACAD. SCI. USA 100 2197–202 , 2002
"... Given a ‘dictionary’ D = {dk} of vectors dk, we seek to represent a signal S as a linear combination S = ∑ k γ(k)dk, with scalar coefficients γ(k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. Previous work considered ..."
Abstract - Cited by 633 (38 self) - Add to MetaCart
optimization problem: specifically, minimizing the ℓ¹ norm of the coefficients γ. In this paper, we obtain parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems. We introduce the Spark, ameasure of linear dependence

Large margin methods for structured and interdependent output variables

by Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract - Cited by 624 (12 self) - Add to MetaCart
the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order
Results 1 - 10 of 84,598