Titlebook: Deep Fusion of Computational and Symbolic Processing; Takeshi Furuhashi,Shun’Ichi Tano,Hans-Arno Jacobse Book 2001 Springer-Verlag Berlin

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書目名稱Deep Fusion of Computational and Symbolic Processing影響因子(影響力)




書目名稱Deep Fusion of Computational and Symbolic Processing影響因子(影響力)學(xué)科排名




書目名稱Deep Fusion of Computational and Symbolic Processing網(wǎng)絡(luò)公開度




書目名稱Deep Fusion of Computational and Symbolic Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Fusion of Computational and Symbolic Processing被引頻次




書目名稱Deep Fusion of Computational and Symbolic Processing被引頻次學(xué)科排名




書目名稱Deep Fusion of Computational and Symbolic Processing年度引用




書目名稱Deep Fusion of Computational and Symbolic Processing年度引用學(xué)科排名




書目名稱Deep Fusion of Computational and Symbolic Processing讀者反饋




書目名稱Deep Fusion of Computational and Symbolic Processing讀者反饋學(xué)科排名

ANTI

978-3-662-00373-2Springer-Verlag Berlin Heidelberg 2001

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https://doi.org/10.1007/978-1-4842-3603-1st (symbolic) and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies neural, reinforcement, and symbolic methods to perform on-line

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https://doi.org/10.1007/978-1-4842-3603-1se methods is studied. A special processor to combine different methods is necessary for integration. It is called an integrator. Among various information-processing methods, only declarative knowledge-based method is suited for an integrator. Then the realistic way of developing the integrator is

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Ablation

Ablation

https://doi.org/10.1007/978-1-4842-3603-1 “fuzzy” sequential knowledge for the description of dynamic characteristics of a system. Symbolic Dynamic System(SDS), a model for symbolic sequences, is extended to deal with “fuzzy” symbolic sequences. This approach introduces topological nature into the symbolic sequences, which allows an interp

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https://doi.org/10.1007/978-1-4842-3603-1 comparison with its predecessor because the learning and the knowledge extraction process are faster and are accomplished in an incremental way . INSS offers a new approach applicable to constructive machine learning with high-performance tools, even in the presence of incomplete or erroneous data.

freight

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