Context-Dependent Similarity

03/27/2013
by   Yizong Cheng, et al.
0

Attribute weighting and differential weighting, two major mechanisms for computing context-dependent similarity or dissimilarity measures are studied and compared. A dissimilarity measure based on subset size in the context is proposed and its metrization and application are given. It is also shown that while all attribute weighting dissimilarity measures are metrics differential weighting dissimilarity measures are usually non-metric.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/09/2019

A new simple and effective measure for bag-of-word inter-document similarity measurement

To measure the similarity of two documents in the bag-of-words (BoW) vec...
research
12/03/2019

Improving upon NBA point-differential rankings

For some time, point-differential has been thought to be a better predic...
research
09/10/2020

Analyze the Effects of Weighting Functions on Cost Function in the Glove Model

When dealing with the large vocabulary size and corpus size, the run-tim...
research
05/19/2022

Why only Micro-F1? Class Weighting of Measures for Relation Classification

Relation classification models are conventionally evaluated using only a...
research
06/24/2020

Practical applications of metric space magnitude and weighting vectors

Metric space magnitude, an active subject of research in algebraic topol...
research
06/22/2021

Notes on the H-measure of classifier performance

The H-measure is a classifier performance measure which takes into accou...
research
05/16/2022

Exploring the Learning Difficulty of Data Theory and Measure

As learning difficulty is crucial for machine learning (e.g., difficulty...

Please sign up or login with your details

Forgot password? Click here to reset