Accession Number : ADA590623


Title :   Second Generation of Mass Estimation


Descriptive Note : Final rept. 15 Sep 2011-14 Sep 2013


Corporate Author : MONASH UNIV CHURCHILL (AUSTRALIA) GIPPSLAND SCHOOL OF INFORMATION TECHNOLOGY


Personal Author(s) : Ting, Kai M


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a590623.pdf


Report Date : 01 Sep 2013


Pagination or Media Count : 174


Abstract : Three progresses were made in the field through mass estimation: (1) the first adaptive version of mass estimation using a new nearest neighbor procedure which runs significantly faster than existing nearest neighbor procedures and needs no indexing schemes, (2) the first mass-based Bayesian classifier which estimates the likelihood directly in multi-dimensional space; unlike existing Bayesian classifiers which estimate simplified surrogates of likelihood (e.g., one-dimensional likelihood), and (3) the first mass-based similarity measure which can be an effective alternative to distance-based similarity measure.


Descriptors :   *ESTIMATES , *MASS , ADAPTIVE SYSTEMS , BAYES THEOREM , MEASUREMENT


Subject Categories : Economics and Cost Analysis
      Statistics and Probability
      Mechanics


Distribution Statement : APPROVED FOR PUBLIC RELEASE