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Parameter Learning for Probabilistic Ontologies
, 2013 Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention. In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human. In this paper we present an approach for learning and tuning the parameters of probabilistic ontologies from data.
RIGUZZI, Fabrizio +3 more
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Learning the learning parameters
[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991A variation of the backpropagation procedure that dynamically adjusts the values of the learning rate and momentum parameters during learning is proposed. These values are made dependent on the standard deviation of the activation distribution of each hidden unit, which allows the network to adapt the parameter values to each individual weight. The new
Pedone, R., Parisi, D.
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INDUCTIVE LEARNING FOR PARAMETER OPTIMIZATION
Cybernetics and Systems, 2000Optimization of simulation model output is one of the most important tasks in a simulation study of a complex system. Efficacy of an optimization approach is expressed in the accuracy of locating a global extremum, as well as in the number of investigated search points.
Rainer Barton, Helena Szczerbicka
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Iteration-wise parameter learning
2011 IEEE Congress of Evolutionary Computation (CEC), 2011Adjusting the control parameters of population-based algorithms is a means for improving the quality of these algorithms' result when solving optimization problems. The difficulty lies in determining when to assign individual values to specific parameters during the run. This paper investigates the possible implications of a generic and computationally
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Parameter optimisation in iterative learning control
2003 European Control Conference (ECC), 2003In this paper parameter optimization through a quadratic performance index is introduced as a method to establish a new iterative learning control law. With this new algorithm, monotonic convergence of the error to zero is guaranteed if the original system is a discrete-time LTI system and it satisfies a positivity condition.
David H. Owens 0001, K. Feng
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Average KR Degrades Parameter Learning
Journal of Motor Behavior, 1996In the present study, the effects of average knowledge of results (KR) about a set of trials on the learning of a spatiotemporal movement pattern were examined. Participants (N = 85) practiced 3 movement patterns with the same relative and absolute timing and the same relative amplitudes but with varied absolute amplitudes.
Wulf, G., Schmidt, R.
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Learning Parameters of Linear Models in Compressed Parameter Space
2012We present a novel method of reducing the training time by learning parameters of a model at hand in compressed parameter space. In compressed parameter space the parameters of the model are represented by fewer parameters, and hence training can be faster.
Yohannes Kassahun +3 more
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Black-box learning of multigrid parameters
Journal of Computational and Applied Mathematics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alexandr Katrutsa +2 more
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Molecular Parameters in Memory and Learning
1966Great biological concepts in the past have not been of the component, molecular type, but rather have been of the holistic, systems type (for example, those of Darwin, Freud, Pavlov). With the expanding scope of the physics and chemistry of large biomolecules (macromolecules) has emerged a new field, “molecular biology,” which ranks conceptually in ...
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On Learning Parameters of Incremental Learning in Chaotic Neural Network
2016The incremental learning is a method to compose an associate memory using a chaotic neural network and provides larger capacity than correlative learning in compensation for a large amount of computation. A chaotic neuron has spatio-temporal sum in it and the temporal sum makes the learning stable to input noise.
Toshinori Deguchi, Naohiro Ishii
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