Difficulties in Forcing Fairness of Polynomial Time Inductive Inference

2009 ◽  
pp. 263-277 ◽  
Author(s):  
John Case ◽  
Timo Kötzing
Keyword(s):  
Polynomial Time ◽  
Inductive Inference

Polynomial Time Inductive Inference of Cograph Pattern Languages from Positive Data

2012 ◽  
pp. 389-404 ◽  
Author(s):  
Yuta Yoshimura ◽  
Takayoshi Shoudai ◽  
Yusuke Suzuki ◽  
Tomoyuki Uchida ◽  
Tetsuhiro Miyahara
Keyword(s):  
Polynomial Time ◽  
Inductive Inference ◽  
Pattern Languages ◽  
Positive Data

Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data

2002 ◽  
pp. 169-184 ◽  
Author(s):  
Yusuke Suzuki ◽  
Ryuta Akanuma ◽  
Takayoshi Shoudai ◽  
Tetsuhiro Miyahara ◽  
Tomoyuki Uchida
Keyword(s):  
Polynomial Time ◽  
Inductive Inference ◽  
Tree Patterns ◽  
Positive Data ◽  
Ordered Tree

Polynomial Time Inductive Inference of Languages of Ordered Term Tree Patterns with Height-Constrained Variables from Positive Data

2017 ◽  
Vol E100.A (3) ◽  
pp. 785-802 ◽  
Author(s):  
Takayoshi SHOUDAI ◽  
Kazuhide AIKOH ◽  
Yusuke SUZUKI ◽  
Satoshi MATSUMOTO ◽  
Tetsuhiro MIYAHARA ◽  
...  
Keyword(s):  
Polynomial Time ◽  
Inductive Inference ◽  
Tree Patterns ◽  
Positive Data

scholarly journals Polynomial Time Inductive Inference of TTSP Graph Languages from Positive Data

2009 ◽  
Vol E92-D (2) ◽  
pp. 181-190 ◽  
Author(s):  
Ryoji TAKAMI ◽  
Yusuke SUZUKI ◽  
Tomoyuki UCHIDA ◽  
Takayoshi SHOUDAI
Keyword(s):  
Polynomial Time ◽  
Inductive Inference ◽  
Positive Data ◽  
Graph Languages

Polynomial Time Inductive Inference of TTSP Graph Languages from Positive Data

2005 ◽  
pp. 366-383 ◽  
Author(s):  
Ryoji Takami ◽  
Yusuke Suzuki ◽  
Tomoyuki Uchida ◽  
Takayoshi Shoudai ◽  
Yasuaki Nakamura
Keyword(s):  
Polynomial Time ◽  
Inductive Inference ◽  
Positive Data ◽  
Graph Languages

The 2-closure of a 32-transitive group in polynomial time

2018 ◽  
Vol 60 (2) ◽  
pp. 360-375
Author(s):  
A. V. Vasil'ev ◽  
D. V. Churikov
Keyword(s):  
Polynomial Time ◽  
Transitive Group

Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

Sign in / Sign up

Export Citation Format

Share Document