Please use this identifier to cite or link to this item:
|Title:||Generality's price inescapable deficiencies in machine-learned programs|
|Citation:||Case, J.,Chen, K.-J.,Jain, S.,Merkle, W.,Royer, J.S. (2003). Generality's price inescapable deficiencies in machine-learned programs. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) 2777 : 684-698. ScholarBank@NUS Repository.|
|Abstract:||This paper investigates some delicate tradeoffs between the generality of an algorithmic learning device and the quality of the programs it learns successfully. There are results to the effect that, thanks to small increases in generality of a learning device, the computational complexity of some successfully learned programs is provably unalterably suboptimal. There are also results in which the complexity of successfully learned programs is asymptotically optimal and the learning device is general, but, still thanks to the generality, some of those optimal, learned programs are provably unalterably information deficient - in some cases, deficient as to safe, algorithmic extractability/provability of the fact that they are even approximately optimal. The paper is on the borderline between learning theory, complexity theory and logic.|
|Source Title:||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.