会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 3. 发明公开
    • METHOD FOR UNSUPERVISED NEURAL NETWORK CLASSIFICATION WITH BACK PROPAGATION
    • 背部变无监督神经网络分类程序
    • EP0724750A1
    • 1996-08-07
    • EP94930805.0
    • 1994-10-17
    • Miles, Inc.
    • ORNSTEIN, Leonard
    • G06N3
    • G06K9/6267G06N3/088Y10S128/925
    • An unsupervised back propagation method for training neural networks. For a set of inputs, target outputs are assigned 1's and 0's randomly or arbitrarily for a small number of outputs. The learning process is initiated and the convergence of outputs towards targets is monitored. At intervals, the learning is paused, and the values for those targets for the outputs which are converging at a less than average rate, are changed (e.g., 0 → 1, or 1 → 0), and the learning is then resumed with the new targets. The process is continuously iterated and the outputs converge on a stable classification, thereby providing unsupervised back propagation. In a further embodiment, samples classified with the trained network may serve as the training sets for additional subdivisions to grow additional layers of a hierarchical classification tree which converges to indivisible branch tips. After training is completed, such a tree may be used to classifiy new unlabelled samples with high efficiency. In yet another embodiment, the unsupervised back propagation method of the present invention may be adapted to classify fuzzy sets.