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

嚙齒動物

Aliaksandr Adamenko,Andrii Fedorenko,Erich Schikutastan. The purpose of?the work was to conduct a comprehensive assessment of the current state of the soil cover in the dried bed of the Aral Sea in order to study the processes that are occurring in the local soils as a result of anthropogenic impacts inducing soil degradation and desertification (e.

索賠

CAMEO

座右銘

Laszlo Barna Iantovics,Corina Rotar,Elena Nechitaf cohesive soils. Hence, relative compaction is used as a governing parameter to represent the different states of natural in situ conditions in cohesive soils. Relative compaction is defined as the ratio of field dry density to the maximum dry density determined by the standard Proctor test. The am

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Handling Concept Drift in Time-Series Data: Meta-cognitive Recurrent Recursive-Kernel OS-ELMLearning Machine with a new modified Drift Detector Mechanism (Meta-RRKOS-ELM-DDM). This model combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine (RKOS-ELM) with the recursive kernel method and a new meta-cognitive learning strategy. We apply Drift Detector Mechani

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Analysis and Application of Step Size of RK4 for Performance Measure of Predictability Horizon of Ch could not have shown the comparison of the performance with other methods. In order to obtain general and absolute performance measure of predictability horizon, this paper analyzes to formulate the relationship between the mean predictability horizon and the step size of the fourth-order Runge-Kut

FLIC

Simultaneous Analysis of Subjective and Objective Data Using Coupled Tensor Self-organizing Maps: WiOM), consists of two tensor SOMs, one of which learns the subjective data while the other learns the objective data. The coupled tensor SOM visualizes the dataset as three maps, namely, one target object map, and two survey item maps corresponding to the subjective and objective data. This method ca

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Marine Multiple Time Series Relevance Discovery Based on Complex Networkne data. At present, a marine measuring point can acquire multiple types of marine data, only by comprehensively using multiple types of ocean data we can more effectively discover the relationship between various ocean measuring points. This paper proposes a mapping method for fusion marine multipl

仇恨

An Effective Lazy Shapelet Discovery Algorithm for Time Series Classification-based models have some obvious drawbacks. First, the progress of shapelet extraction is time consuming. Second, the shapelets discovered are merely good on average for the training instances, while local features of each instance to be classified are neglected. For that, instance selection strategy

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