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

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成份

BayesGrad: Explaining Predictions of Graph Convolutional Networks“how do we explain the predictions of graph convolutional networks?” A possible approach to answer this question is to visualize evidence substructures responsible for the predictions. For chemical property prediction tasks, the sample size of the training data is often small and/or a label imbalanc

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Protein

Research on the Prediction Technology of Ice Hockey Based on Support Vector Machine input characteristics and parameter selection of the model have important influence on the prediction performance. Based on this, this paper proposes a support vector machine ice hockey situation prediction model based on principal component analysis, hybrid genetic algorithm and particle swarm opt

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Deep Structure of Gaussian Kernel Function Networks for Predicting Daily Peak Power Demandshe prediction model, the whole time series is divided into multiple parts and each part is trained using a GKFN. Then, the trained GKFNs are combined using the deep structure of GKFNs to minimize the mean square errors (MSEs) of prediction model. As a consequence, the proposed deep structure of GKFN

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Convolutional Model for Predicting SNP Interactionsorts have been contributed by researchers to study the interaction effects between multi-locus SNPs for discerning the status of complex diseases. However, the current conventional machine learning techniques are still left with several caveats. Deep learning is a new breed of machine learning techn

Adulterate

Financial Data Forecasting Using Optimized Echo State Networkl non-linear computing ability, it has been applied to predict the time series. However, the parameters of the ESN need to be set experimentally, which can lead to instable performance and there is space to further improve its performance. In order to address this challenge, an improved fruit fly op

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ESO

crucial

Research on Usage Prediction Methods for O2O Couponsses. But without careful analysis, large amount of coupons can be wasted because of inappropriate delivery strategies. In this era of big data, O2O coupons can be more precisely delivered by using history usage records of customers. By implementing the mainstream data mining and machine learning mod