摘要(英) |
In recent years, many people try to understand the structure of the brain. We believed that if we can add machines to learning. It is possible to use machines instead of humans and do some simple, repetitive things.
This neural network architecture is a combination of supervised and unsupervised neural networks. This paper uses the k-means algorithm to group the input image. And then use the self-organizing map to generate feature map. And then use adaptive resonance theory to save the results of response map. Finally, use the learning vector quantization network fine-tuning the results of adaptive resonance theory without the use of Gradient. In addition, we can visualize and transform each layer of features into images that can be understood by the human’s eyes.
In this paper’s experiments, we use two types of datasets. One is MNIST, another is song datasets. The experiment includes the feature maps, the alert parameters of the adaptive resonance theory, binary of image, and image combination. In the end, the feature is visualized presented
The experiment in this thesis has compared to the feature maps, the alert parameters of ART, the image binary, and the different method of the image’s merger. |
參考文獻 |
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