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Article | 06-July-2017


advantageous to build local regression models than a global model. Additionally, we propose a local feature selection via regularization. The empirical research carried out on three data sets from real estate market confirmed the effectiveness of this approach. We paid special attention to the model quality assessment using cross-validation for estimation of the residual standard error.

Mariusz Kubus

Statistics in Transition New Series, Volume 17 , ISSUE 3, 515–524

Article | 30-November-2018

Street View House Number Identification Based on Deep Learning

. OPTIMIZERS TRAINING RESULTS optimizer Top Accuracy/% SGD 87.350184 Adam 89.090000 Adamax 88.955900 RMSprop 88.676000 C. Comparison of the application of L2 regularization with the appropriate weight attenuation coefficient In the face of possible over-fitting, one possible inhibitory measure is the introduction of regularization. We first use the L2 regularization and introduce the training set accuracy rate, training set loss, test set loss three indicators to enrich the

Haoqi Yang, Hongge Yao

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 3, 47–52

Article | 11-January-2021

Conditional GAN-based Remote Sensing Target Image Generation Method

original background, the L1 regularization loss is added to the loss term of the generated model G. The loss function is defined as shown in formula 3. (3) LL1(G)=Ex,y,z(∥y−G(x∣y)∥1) The final objective loss function becomes as shown in formula 4. (4) G∗=arg⁡minGmaxDLcGAN(G,D)+λLL1(G) III. VERIFICATION EXPERIMENT AND COMPARATIVE ANALYSIS In order to verify whether the conditional generation model in this paper can generate remote sensing target images in non-training samples, and to verify the

Haoyang Liu, Zhiyi Hu, Jun Yu, Shouyi Gao

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 4, 66–74

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