Unconfused ultraconservative multiclass algorithms
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 60547852X | ||
| 003 | CHVBK | ||
| 005 | 20210128100406.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150501xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5490-3 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5490-3 | ||
| 245 | 0 | 0 | |a Unconfused ultraconservative multiclass algorithms |h [Elektronische Daten] |c [Ugo Louche, Liva Ralaivola] |
| 520 | 3 | |a We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago where the proposed approaches to combat the noise revolve around a Perceptron learning scheme fed with peculiar examples computed through a weighted average of points from the noisy training set. We propose to build upon these approaches and we introduce a new algorithm called Unconfused Multiclass additive Algorithm (U MA) which may be seen as a generalization to the multiclass setting of the previous approaches. In order to characterize the noise we use the confusion matrix as a multiclass extension of the classification noise studied in the aforementioned literature. Theoretically well-founded, U MA furthermore displays very good empirical noise robustness, as evidenced by numerical simulations conducted on both synthetic and real data. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Multiclass classification |2 nationallicence | |
| 690 | 7 | |a Perceptron |2 nationallicence | |
| 690 | 7 | |a Noisy labels |2 nationallicence | |
| 690 | 7 | |a Confusion Matrix |2 nationallicence | |
| 690 | 7 | |a Ultraconservative algorithms |2 nationallicence | |
| 700 | 1 | |a Louche |D Ugo |u Qarma, Lab. d'Informatique Fondamentale de Marseille, CNRS, Université d'Aix-Marseille, Marseille, France |4 aut | |
| 700 | 1 | |a Ralaivola |D Liva |u Qarma, Lab. d'Informatique Fondamentale de Marseille, CNRS, Université d'Aix-Marseille, Marseille, France |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 99/2(2015-05-01), 327-351 |x 0885-6125 |q 99:2<327 |1 2015 |2 99 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5490-3 |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/s10994-015-5490-3 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Louche |D Ugo |u Qarma, Lab. d'Informatique Fondamentale de Marseille, CNRS, Université d'Aix-Marseille, Marseille, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ralaivola |D Liva |u Qarma, Lab. d'Informatique Fondamentale de Marseille, CNRS, Université d'Aix-Marseille, Marseille, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 99/2(2015-05-01), 327-351 |x 0885-6125 |q 99:2<327 |1 2015 |2 99 |o 10994 | ||