Titlebook: Neural Information Processing; 23rd International C Akira Hirose,Seiichi Ozawa,Derong Liu Conference proceedings 2016 Springer Internationa

发怨言

A Problem in Model Selection of LASSO and Introduction of Scalingge at a sparse representation. This problem is important because it directly affects a quality of model selection in LASSO. We derived a prediction risk for LASSO with scaling and obtained an optimal scaling parameter value that minimizes the risk. We then showed the risk is improved by assigning th

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Evolutionary Multi-task Learning for Modular Training of Feedforward Neural Networksuro-evolution has shownpromising performance for a number of real-world applications. Recently, evolutionary multi-tasking has been proposed for optimisation problems. In this paper, we present a multi-task learning for neural networks that evolves modular network topologies. In the proposed method,

摊位

On the Noise Resilience of Ranking Measuresetic and real-world data sets, we investigated the resilience to noise of various ranking measures. Our experiments revealed that the area under the ROC curve (AUC) and a related measure, the truncated average Kolmogorov-Smirnov statistic (taKS), can reliably discriminate between models with truly d

Pde5-Inhibitors

做作

Group Dropout Inspired by Ensemble Learnings a large number of layers and a huge number of units and connections, so overfitting occurs. Dropout learning is a kind of regularizer that neglects some inputs and hidden units in the learning process with a probability .; then, the neglected inputs and hidden units are combined with the learned n

FUSE

STELL

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Sampling-Based Gradient Regularization for Capturing Long-Term Dependencies in Recurrent Neural Netwatives. We construct an analytical framework to estimate a contribution of each training example to the norm of the long-term components of the target functions gradient and use it to hold the norm of the gradients in the suitable range. Using this subroutine we can construct mini-batches for the st

Pessary

Face Hallucination Using Correlative Residue Compensation in a Modified Feature Spacebeen proposed due to its neighborhood preserving nature. However, the projection of low resolution (LR) image to high resolution (HR) is “one-to-multiple” mapping; therefore manifold assumption does not hold well. To solve the above inconsistency problem we proposed a new approach. First, an interme