Title :
Finding the Most Probable Ranking of Objects with Probabilistic Pairwise Preferences
Author :
Parakhin, Mikhail ; Haluptzok, Patrick
Author_Institution :
Microsoft Corp, Redmond, WA, USA
Abstract :
This paper discusses the ranking of a set of objects when a possibly inconsistent set of pairwise preferences is given.We consider the task of ranking objects when pairwise preferences not only can contradict each other, but in general are not binary-meaning, for each pair of objects the preference is represented by a pair of non-negative numbers that sum up to one and can be viewed as a confidence in our belief that one object is preferable to the other in the absence of any other information. We propose a probability function on the sequence of objects that includes non-binary preferences and evaluate methods for finding the most probable ranking for this model using it to rank results of a Microsoft on-line handwriting recognizer.
Keywords :
graph theory; learning (artificial intelligence); probability; Microsoft online handwriting recognizer; graph theory; nonbinary preference; nonnegative number; probabilistic pairwise preference learning algorithm; probable object ranking model; Classification algorithms; Frequency; Handwriting recognition; Machine learning; Performance loss; Sorting; Text analysis; Time measurement; Vocabulary; Bradley-Terry; pairwise; ranking;
Conference_Titel :
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-4500-4
Electronic_ISBN :
1520-5363
DOI :
10.1109/ICDAR.2009.228