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

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Conference proceedings 2016e; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms..

mortgage

Chaotic Feature Selection and Reconstruction in Time Series Predictiond has to ensure that important information has not been lost by with feature selection for data reduction. We present a chaotic feature selection and reconstruction method based on statistical analysis for time series prediction. The method can also be viewed as a way for reduction of data through s

Synchronism

完整

建筑師

Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecastingon of researchers recently. However, the learning algorithm used in DL is usually with the famous error-backpropagation (BP) method. In this paper, we adopt a reinforcement learning (RL) algorithm “Stochastic Gradient Ascent (SGA)” proposed by Kimura and Kobayashi into a Deep Belief Net (DBN) with m

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Neuron-Network Level Problem Decomposition Method for Cooperative Coevolution of Recurrent Networks ecomposition methods used in cooperative coevolution are synapse and neuron level. The combination of both the problem decomposition as a hybrid problem decomposition has been seen applied in time series prediction. The different problem decomposition methods applied at particular area of a network

誘導(dǎo)

placebo-effect

傀儡

Combining Deep Learning and Preference Learning for Object Tracking In order to build a tracking system, this paper proposes to combine two different learning frameworks: deep learning and preference learning. On the one hand, deep learning is used to automatically extract latent features for describing the multi-dimensional raw images. Previous research has shown

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