Machine learning with unrestricted classification based on knowledge base

Machine learning classification models are exceedingly large, complex and lack universality, not only consuming significant resources and posing difficulties in training, but also lacking in complexity, cannot achieve unrestricted classification. The academic community has attempted to solve the problem of unrestricted classification through lifelong learning and meta-learning, but these methods have limitations. The unrestricted classification of images is a complex task, inherently possessing infinite complexity. Consequently, systems designed for unrestricted classification ought to possess infinite complexity as well. Therefore, the current large models without knowledge bases are actually insufficient in complexity for unrestricted classification. This paper addresses the issue of infinite complexity through the utilization of a knowledge base approach. Knowledge bases can be conveniently expanded and simply arranged to achieve infinite complexity. Based on the analogy principle, this article adopts two methods to build two image feature knowledge bases, and demonstrates the application of multimodal image unrestricted classification. This study conducts classification verification on a total of 270,000 images across 56 classes. The accuracy rates of both knowledge base construction methods are over 99%.