全部文献期刊学位论文会议报纸专利标准年鉴图书|学者科研项目
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作者:Yiannis Kokkinos , Konstantinos G. Margaritis
来源:[J].Neural Computing and Applications(IF 1.168), 2017, Vol.28 (6), pp.1309-1328Springer
摘要:Local learning algorithms use a neighborhood of training data close to a given testing query point in order to learn the local parameters and create on-the-fly a local model specifically designed for this query point. The local approach delivers breakthrough performance in many a...
作者:Yiannis Kokkinos , Konstantinos G. Margaritis
来源:[J].Neurocomputing(IF 1.634), 2018, Vol.295, pp.29-45Elsevier
摘要:Abstract(#br)The typical model selection strategy applied in most Extreme Learning Machine (ELM) papers depends on a k -fold cross-validation and a grid search to select the best pair { L, C } of two adjustable hyper-parameters, namely the number L of hidden ELM nodes and the reg...
作者:Yiannis Kokkinos , Konstantinos G. Margaritis
来源:[J].Neurocomputing(IF 1.634), 2015, Vol.150, pp.513-528Elsevier
摘要:Abstract(#br)We consider distributed privacy-preserving data mining in large decentralized data locations which can build several neural networks to form an ensemble. The best neural network classifiers are selected via the proposed confidence ratio affinity propagation in an asy...
作者:Yiannis Kokkinos , Konstantinos G. Margaritis
来源:[J].Information Sciences(IF 3.643), 2014Elsevier
摘要:Abstract(#br)This paper presents a Hierarchical Markovian Radial Basis Function Neural Network (HiMarkovRBFNN) model that enables recursive operations. The hierarchical structure of this network is composed of recursively nested RBF Neural Networks with arbitrary levels of hierar...
作者:Yiannis Kokkinos , Konstantinos G. Margaritis
来源:[J].Artificial Intelligence Review(IF 1.565), 2014, Vol.42 (3), pp.385-402Springer
摘要:Abstract(#br)For distributed data mining in peer-to-peer systems this work describes a completely asynchronous, scalable and privacy-preserving committee machine. Regularization neural networks are used for all the Peer classifiers and the combiner committee in an embedded archit...
作者:Yiannis Kokkinos , Konstantinos G. Margaritis
来源:[J].Journal of Computational Science, 2018, Vol.25, pp.260-279Elsevier
摘要:Abstract(#br)We present a new scalable Probabilistic Neural Network (PNN) construction method suitable for data-neuron parallelism in a ring pipeline parallel topology that allows training a large scale distributed model on a large scale distributed dataset. First the recently pr...
作者:Yiannis Kokkinos , Theodoros Panagiotakos ...
来源:[J].Journal of Veterinary Internal Medicine(IF 2.064), 2019, Vol.33 (6), pp.2644-2656Wiley
摘要:Abstract(#br)Background(#br)Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.(#br)Hypothesis/Objectives(#br)To derive a model to predict the risk of cats developing chro...
作者:Yiannis Kokkinos , Konstantinos G. Margaritis
来源:[J].Computational Intelligence(IF 1), 2018, Vol.34 (3), pp.875-894Wiley
摘要:Abstract(#br)This work considers scalable incremental extreme learning machine (I‐ELM) algorithms, which could be suitable for big data regression. During the training of I‐ELMs, the hidden neurons are presented one by one, and the weights are based solely on simple direct s...

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