Results 231 to 240 of about 1,421,625 (273)

(Machine) learning parameter regions

Journal of Econometrics, 2021
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Montiel Olea, José Luis, Nesbit, James
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Parameter Learning for Alpha Integration

Neural Computation, 2013
In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed [Formula: see text]-integration as a principled way of blending multiple positive measures (e.g., stochastic ...
Choi, H, Choi, S, Choe, Y
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Average KR Degrades Parameter Learning

Journal of Motor Behavior, 1996
In 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 the learning parameters

[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991
A 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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Parameter Search Transfer Learning

2023
Deep learning approaches have had success in many domains recently, particularly in domains with large amounts of training data. However, there are domains without a sufficient quantity of training data, or where the training data present is of insufficient quality.
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Parameter optimisation in iterative learning control

2003 European Control Conference (ECC), 2003
In 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.
D. H. Owens, K. Feng
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KNN parameter selection via meta learning

2013 21st Signal Processing and Communications Applications Conference (SIU), 2013
In this study, the K Nearest Neighbor's parameter k is predicted by system. Meta learning method is used for prediction. Getting training set with meta-features, 200 data sets were used. For each of them, 16 meta-features were extracted. The K Nearest Neighbour algorithm was applied each of them with most common 6 k values the best one is selected ...
Özger, Zeynep Banu, AMASYALI, Mehmet Fatih   +1 more
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Physiological parameters and learning

30th Annual Frontiers in Education Conference. Building on A Century of Progress in Engineering Education. Conference Proceedings (IEEE Cat. No.00CH37135), 2002
Whilst much attention is paid to the quality and the avenues of presentation of educational material, there seems to be little consideration given to human physiological processes of information ingestion and assimilation when planning the educational experience.
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Iteration-wise parameter learning

2011 IEEE Congress of Evolutionary Computation (CEC), 2011
Adjusting 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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