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

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21#
發(fā)表于 2025-3-25 06:46:28 | 只看該作者
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..
22#
發(fā)表于 2025-3-25 09:24:29 | 只看該作者
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
23#
發(fā)表于 2025-3-25 12:30:36 | 只看該作者
24#
發(fā)表于 2025-3-25 19:41:37 | 只看該作者
25#
發(fā)表于 2025-3-25 23:17:15 | 只看該作者
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
26#
發(fā)表于 2025-3-26 03:28:49 | 只看該作者
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
27#
發(fā)表于 2025-3-26 04:44:32 | 只看該作者
28#
發(fā)表于 2025-3-26 08:58:28 | 只看該作者
29#
發(fā)表于 2025-3-26 15:46:25 | 只看該作者
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
30#
發(fā)表于 2025-3-26 16:49:23 | 只看該作者
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