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24,303
The geometry of algorithms with orthogonality constraints
- SIAM J. MATRIX ANAL. APPL
, 1998
"... In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal proces ..."
Abstract
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Cited by 640 (1 self)
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In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal
Structural Constraints in
"... For hypertext to be successful in the area of software engineering, it is necessary to have tools that can manipulate a hypertext of typed nodes, links, and composites. In several systems it is possible to define new application specific types. The type of a hypertext entity will be used to model th ..."
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the role of the entity in the overall hypertext. As part of a type description, one will often say what attributes the entity will have. This paper concerns itself with a different problem: how do we specify the domain specific constraints on the structure of the software hypertext. Much research
1 The Coordinate Structure Constraint
"... As is well known, the Coordinate Structure Constraint (CSC) was first pro- ..."
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As is well known, the Coordinate Structure Constraint (CSC) was first pro-
Large margin methods for structured and interdependent output variables
- 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 ..."
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Cited by 624 (12 self)
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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
Analogical Mapping by Constraint Satisfaction
- COGNITIVE SCIENCE 13, 29-5 (1989)
, 1989
"... A theory of analogical mopping between source and target analogs based upon interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of fsomorphfsm encourages mappings that maximize the consistency of relational corresondences between the elements of th ..."
Abstract
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Cited by 397 (29 self)
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A theory of analogical mopping between source and target analogs based upon interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of fsomorphfsm encourages mappings that maximize the consistency of relational corresondences between the elements
Structural Constraint Satisfaction
- In Papers from the AAAI-99 Workshop on Configuration, Technical Report
, 1999
"... Abstract Conventional constraint satisfaction problem (CSP) formulations are static. There is a given set of constraints and variables, and the structure of the constraint graph does not change. For a lot of search problems, though, it is not clear in advance what a solution's constraint graph ..."
Abstract
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Cited by 15 (3 self)
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Abstract Conventional constraint satisfaction problem (CSP) formulations are static. There is a given set of constraints and variables, and the structure of the constraint graph does not change. For a lot of search problems, though, it is not clear in advance what a solution's constraint
Learnability in Optimality Theory
, 1995
"... In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given gr ..."
Abstract
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Cited by 529 (35 self)
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In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given
The structure of the potassium channel: molecular basis of K+ conduction and selectivity
- Science
, 1998
"... The potassium channel from Streptomyces lividans is an integral membrane protein with sequence similarity to all known K1 channels, particularly in the pore region. X-ray analysis with data to 3.2 angstroms reveals that four identical subunits create an inverted teepee, or cone, cradling the selecti ..."
Abstract
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Cited by 460 (1 self)
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in the pore at the center of the bilayer. Main chain carbonyl oxygen atoms from the K1 channel signature sequence line the selectivity filter, which is held open by structural constraints to co-ordinate K1 ions but not smaller Na1 ions. The selectivity filter contains two K1 ions about 7.5 angstroms apart
The TSIMMIS Project: Integration of Heterogeneous Information Sources
"... The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects, ..."
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Cited by 535 (19 self)
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The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects
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