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 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.

収録刊行物

  • 日本神経回路学会誌 = The Brain & neural networks

    日本神経回路学会誌 = The Brain & neural networks 3(2), 43-50, 1996-06-05

    Japanese Neural Network Society

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各種コード

  • NII論文ID(NAID)
    10008841133
  • NII書誌ID(NCID)
    AA11658570
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    1340766X
  • データ提供元
    CJP書誌  J-STAGE 
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