Results 231 to 240 of about 701,150 (283)

Pacemaker Parameter Tuning using Crescendo

, 2015
Gamble, Carl, Mansfield, Martin, Fitzgerald, John, Larsen, Peter Gorm   +3 more
openaire   +1 more source

Tuning and clinical application of large language models in Traditional Chinese Medicine: scoping review. [PDF]

Chin Med
Han C, Yang G, Li H, Zhu L, Feng M.
europepmc   +1 more source

Explainable hybrid AI CAD framework for advanced prediction of steel surface defects. [PDF]

Sci Rep
Moon C, Al-Antari MA, Gu YH.
europepmc   +1 more source

Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models. [PDF]

Front Digit Health
AlSaad R, Alshakhs S, Thomas R.
europepmc   +1 more source

Anatomy-guided visual prompt tuning for cross-modal breast cancer understanding. [PDF]

NPJ Digit Med
Zhao S, Meng Q, He Y, Xu X, Zhu J, Qiu J, Wu C, Han Y, Deng J, Pan T, Liu J.   +10 more
europepmc   +1 more source

Analysis Model for Infant Incubator Adverse Events Using Retrieval-Augmented Generation Combined With Dual-Adapter Fine-Tuning: Development and Evaluation Study.

JMIR Med Inform
Xia W, Zhu W, Li T, Wang L, Li W, Zhang P.   +5 more
europepmc   +1 more source

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Parameter tuning for meta-heuristics

Knowledge-Based Systems, 2020
Abstract These days meta-heuristic algorithms are gaining lot of popularity. The performance of the meta-heuristics depends upon the suitable selection of user dependent parameters. Finding the most suitable values for the parameters (fine tuning) is a challenging problem.
Susheel Kumar Joshi, Jagdish Chand Bansal   +1 more
openaire   +3 more sources

Multiobjectivization for classifier parameter tuning

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013
We present a multiobjectivization approach to the parameter tuning of RBF networks and multilayer perceptrons. The approach works by adding two new objectives -- maximization of kappa statistic and minimization of root mean square error -- to the originally single-objective problem of minimizing the classification error of the model.
Pilát, M., Neruda, R. (Roman)
openaire   +2 more sources

Tuning-Parameter Calibration

2021
Regularized estimators consist of two terms, one for comparing model parameters to data and one for including prior information. The tuning parameters define the weighting: small tuning parameters emphasize the data, while large tuning parameters emphasize the prior information.
openaire   +1 more source

Parameters and Parameter Tuning

2015
Chapter 3 presented an algorithmic framework that forms the common basis for all evolutionary algorithms. A decision to use an evolutionary algorithm implies that the user adopts the main design decisions behind this framework. Thus, the main algorithm setup follows automatically: the algorithm is based on a population of candidate solutions that is ...
A. E. Eiben, J. E. Smith
openaire   +1 more source