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Artificial intelligence neural network apparatus and data classification method with visualized feature vector

机译:基于可视化特征向量的人工智能神经网络装置及数据分类方法

摘要

An artificial intelligence neural network apparatus, comprising: a labeled learning database having data of a feature vector composed of N elements; a first feature vector image converter configured to visualize the data in the learning database to form an imaged learning feature vector image database; a deep-learned artificial intelligence neural network configured to use a learning feature vector image in the learning feature vector image database to perform an image classification operation; an inputter configured to receive a test image, and generate test data based on the feature vector; and a second feature vector image converter configured to visualize the test data and convert the visualized test data into a test feature vector image. The deep-learned artificial intelligence neural network is configured to determine a class of the test feature vector image.
机译:一种人工智能神经网络装置,包括:标记学习数据库,其具有由N个元素组成的特征向量的数据;第一特征向量图像转换器,其被配置为可视化学习数据库中的数据以形成成像的学习特征向量图像数据库;深度学习的人工智能神经网络,其被配置为使用学习特征向量图像数据库中的学习特征向量图像来执行图像分类操作;输入器,用于接收测试图像,并基于所述特征向量生成测试数据;以及第二特征向量图像转换器,其被配置为使测试数据可视化并将可视化的测试数据转换为测试特征向量图像。将深度学习的人工智能神经网络配置为确定一类测试特征向量图像。

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