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Inductive Inference over MacroMolecules. L. Allison, C. S. Wallace and C. N. Yee.
 

Summary: ­
Inductive Inference over Macro­Molecules.
L. Allison, C. S. Wallace and C. N. Yee.
Department of Computer Science,
Monash University,
Australia 3168.
uucp: xxx@bruce.cs.monash.oz.au xxx=[lloyd, csw, cyee]
Supported by Australian Research Council grant A48830856
1. Introduction.
Minimum message length (MML) encoding techniques
are applied to inductive inference problems over strings
derived from sequencing biological macro­
molecules[1,2]. The paper is framed in terms of DNA
strings but the techniques apply to proteins and other
strings.
It is assumed that any one string is random in the sense
of Kolmogorov complexity. This is very nearly the case
for biological macro­molecules and it is certainly very
difficult to encode a typical DNA string in much less than
two bits per character. The assumption is not essential to

  

Source: Allison, Lloyd - Caulfield School of Information Technology, Monash University
Dowe, David - Caulfield School of Information Technology, Monash University

 

Collections: Computer Technologies and Information Sciences