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ARTIFICIAL INTELLIGENCE NEURAL NETWORK APPARATUS AND DATA CLASSIFICATION METHOD WITH VISUALIZED FEATURE VECTOR

机译:具有可视特征向量的人工智能神经网络设备和数据分类方法

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:标记的学习数据库,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被配置为可视化学习数据库中的数据以形成成像学习功能矢量图像数据库;深度学习的人工智能神经网络被配置为在学习中使用学习特征向量图像传染媒介图像数据库来执行图像分类操作;输入器被配置为接收测试图像,并基于特征向量生成测试数据;第二特征向量图像转换器被配置为可视化测试数据并将可视化测试数据转换为测试特征向量图像。深度学习的人工智能神经网络被配置为确定测试特征矢量图像的一类。

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