General limitations on machine learning
Achim G. Hoffmann · 1990
Machine learning is widely regarded as a tool for overcoming the bottleneck in knowledge acquisition. Especially in knowledge-intensive domains there is the hope for using machine learning techniques successfully. This paper prove the general inability of simple learning programs to learn complex concepts from few input data. This holds independently of the epistemological problems of inductive inference. These results are obtained by the use of algorithmic information theory.