2017/05/31 by Chuyu Xiong, Xiong, Chuyu
Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Metallurgy and Material Forming #Robot Manipulation and Learning #Robotic Mechanisms and Dynamics
paper · pdf · doi:10.48550/arxiv.1706.00066
openalex publication_date 2017/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In [1], we introduced mechanical learning and proposed 2 approaches to mechanical learning. Here, we follow one such approach to well describe the objects and the processes of learning. We discuss 2 kinds of patterns: objective and subjective pattern. Subjective pattern is crucial for learning machine. We prove that for any objective pattern we can find a proper subjective pattern based upon least base patterns to express the objective pattern well. X-form is algebraic expression for subjective pattern. Collection of X-forms form internal representation space, which is center of learning machine. We discuss learning by teaching and without teaching. We define data sufficiency by X-form. We then discussed some learning strategies. We show, in each strategy, with sufficient data, and with certain capabilities, learning machine indeed can learn any pattern (universal learning machine). In appendix, with knowledge of learning machine, we try to view deep learning from a different angle, i.e. its internal representation space and its learning dynamics.