学習時間に制約がある環境に適したニューラルネットワーク構造学習則

書誌事項

タイトル別名
  • The Adaptive Network Architecture for a Time-constrained Environment

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This paper proposes an adaptive network architecture called Hybrid SOLAR (Supervised One-shot Learning Algorithm for Real number inputs), which is a hybrid algorithm between a one-shot network construction algorithm and an iterative pruning algorithm. Hybrid SOLAR determines a network structure in two stages. In the first stage, Hybrid SOLAR requires only a single presentation of training examples to construct the network and learning is finished. In the second stage, the network prunes redundant weights to improve the generalization ability. Thus, Hybrid SOLAR retains the advantages of those two algorithms. It needs only a single presentation of the training set for learning and the generalization ability is satisfactory.

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詳細情報 詳細情報について

  • CRID
    1390001204466709888
  • NII論文ID
    10008841133
  • NII書誌ID
    AA11658570
  • DOI
    10.3902/jnns.3.43
  • ISSN
    18830455
    1340766X
  • データソース種別
    • JaLC
    • Crossref
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用不可

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