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466,258
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
Abstract

Cited by 816 (39 self)
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learned from relational databases by iteratively optimizing a pseudolikelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a realworld database and knowledge base in a university domain illustrate the promise of this approach.
Heterogeneous uncertainty sampling for supervised learning
 In Proceedings of the 11th International Conference on Machine Learning (ICML
, 1994
"... Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suit ..."
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Cited by 312 (3 self)
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suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach
iteration
, 2014
"... Approximate solutions of a timefractional diffusion equation with a source term using the variational ..."
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Approximate solutions of a timefractional diffusion equation with a source term using the variational
FastHenry: A MultipoleAccelerated 3D Inductance Extraction Program
, 1993
"... ... based on mesh analysis can be combined with a GMRESstyle iterative matrix solution technique to make a reasonably fast 3D frequency dependent inductance and resistance extraction algorithm. Unfortunately, both the computation time and memory re quired for that approach grow faster than n 2, w ..."
Abstract

Cited by 216 (48 self)
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... based on mesh analysis can be combined with a GMRESstyle iterative matrix solution technique to make a reasonably fast 3D frequency dependent inductance and resistance extraction algorithm. Unfortunately, both the computation time and memory re quired for that approach grow faster than n 2
Spontaneous evolution of linguistic structure: an iterated learning model of the emergence of regularity and irregularity
 IEEE Transactions on Evolutionary Computation
, 2001
"... Abstract—A computationally implemented model of the transmission of linguistic behavior over time is presented. In this model [the iterated learning model (ILM)], there is no biological evolution, natural selection, nor any measurement of the success of the agents at communicating (except for result ..."
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Cited by 144 (18 self)
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Abstract—A computationally implemented model of the transmission of linguistic behavior over time is presented. In this model [the iterated learning model (ILM)], there is no biological evolution, natural selection, nor any measurement of the success of the agents at communicating (except
Inductive and Coinductive types with Iteration and Recursion
 Proceedings of the 1992 Workshop on Types for Proofs and Programs, Bastad
, 1992
"... We study (extensions of) simply and polymorphically typed lambda calculus from a point of view of how iterative and recursive functions on inductive types are represented. The inductive types can usually be understood as initial algebras in a certain category and then recursion can be defined in ter ..."
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Cited by 59 (1 self)
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We study (extensions of) simply and polymorphically typed lambda calculus from a point of view of how iterative and recursive functions on inductive types are represented. The inductive types can usually be understood as initial algebras in a certain category and then recursion can be defined
Determinacy of Wadge classes in the Baire space and simple iteration
"... of inductive definition ..."
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