Efficient $$F$$ F measure maximization via weighted maximum likelihood
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 60547799X | ||
| 003 | CHVBK | ||
| 005 | 20210128100403.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150301xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-014-5439-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5439-y | ||
| 245 | 0 | 0 | |a Efficient $$F$$ F measure maximization via weighted maximum likelihood |h [Elektronische Daten] |c [Georgi Dimitroff, Georgi Georgiev, Laura Toloşi, Borislav Popov] |
| 520 | 3 | |a The classification models obtained via maximum likelihood-based training do not necessarily reach the optimal $$F_\beta $$ F β -measure for some user's choice of $$\beta $$ β that is achievable with the chosen parametrization. In this work we link the weighted maximum entropy and the optimization of the expected $$F_\beta $$ F β -measure, by viewing them in the framework of a general common multi-criteria optimization problem. As a result, each solution of the expected $$F_\beta $$ F β -measure maximization can be realized as a weighted maximum likelihood solution within the maximum entropy model - a well understood and behaved problem for which standard (off the shelf) gradient methods can be used. Based on this insight, we present an efficient algorithm for optimization of the expected $$F_\beta $$ F β using weighted maximum likelihood with dynamically adaptive weights. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Maximum Entropy |2 nationallicence | |
| 690 | 7 | |a Acceptance Threshold |2 nationallicence | |
| 690 | 7 | |a Maximum Entropy Model |2 nationallicence | |
| 690 | 7 | |a Weighted Likelihood |2 nationallicence | |
| 690 | 7 | |a Brute Force Approach |2 nationallicence | |
| 700 | 1 | |a Dimitroff |D Georgi |u Ontotext AD, Sofia, Bulgaria |4 aut | |
| 700 | 1 | |a Georgiev |D Georgi |u Ontotext AD, Sofia, Bulgaria |4 aut | |
| 700 | 1 | |a Toloşi |D Laura |u Ontotext AD, Sofia, Bulgaria |4 aut | |
| 700 | 1 | |a Popov |D Borislav |u Ontotext AD, Sofia, Bulgaria |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/3(2015-03-01), 435-454 |x 0885-6125 |q 98:3<435 |1 2015 |2 98 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5439-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5439-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Dimitroff |D Georgi |u Ontotext AD, Sofia, Bulgaria |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Georgiev |D Georgi |u Ontotext AD, Sofia, Bulgaria |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Toloşi |D Laura |u Ontotext AD, Sofia, Bulgaria |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Popov |D Borislav |u Ontotext AD, Sofia, Bulgaria |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/3(2015-03-01), 435-454 |x 0885-6125 |q 98:3<435 |1 2015 |2 98 |o 10994 | ||