Descriptions of Objectives and Processes of Mechanical Learning
This provides a theoretical foundation for understanding learning machines, though it appears incremental as it builds on prior work introducing mechanical learning.
The paper formalizes mechanical learning by proving that any objective pattern can be expressed through subjective patterns using X-forms, establishing an internal representation space as the core of learning machines, and demonstrating universal learning capabilities under sufficient data and certain conditions.
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.