Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

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印第安人

BayesGrad: Explaining Predictions of Graph Convolutional NetworksayesGrad successfully visualizes the substructures responsible for the label prediction in the artificial experiment, even when the sample size is small. Furthermore, we use a real dataset to evaluate the effectiveness of the visualization. The basic idea of BayesGrad is not limited to graph-structu

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Curmudgeon

Convolutional Model for Predicting SNP Interactionsal network is trained to identify true causative two-locus SNP interactions. The performance of the method is evaluated on hypertension data. Highly ranked two-locus SNP interactions are identified for the manifestation of hypertension.

constellation

Application of SMOTE and LSSVM with Various Kernels for Predicting Refactoring at Method Leveloposed approach. LSSVM with SMOTE data imbalance technique are being utilized in order to overcome the class imbalance problem. . Analysis of the results reveals that LS-SVM with RBF kernel using SMOTE results in the best performance.

growth-factor

Daiki Ito,Raku Shirasawa,Shinnosuke Hattori,Shigetaka Tomiya,Gouhei Tanaka

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Conference proceedings 201818, held in Siem Reap, Cambodia, in December 2018..The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different

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陶瓷

Financial Data Forecasting Using Optimized Echo State Networkocess of the swarm is transformed from two-dimensional to three-dimensional space. The proposed approach is applied to financial data sets. Experimental results show that the proposed FOA-ESN and IFOA-ESN models are more effective (~50% improvement) than others, and the IFOA-ESN can obtain the best prediction accuracy.