Soft-max boosting
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605478252 | ||
| 003 | CHVBK | ||
| 005 | 20210128100404.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5491-2 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5491-2 | ||
| 100 | 1 | |a Geist |D Matthieu |u IMS - MaLIS Research Group and UMI 2958 (GeorgiaTech-CNRS), CentraleSupélec, Metz, France |4 aut | |
| 245 | 1 | 0 | |a Soft-max boosting |h [Elektronische Daten] |c [Matthieu Geist] |
| 520 | 3 | |a The standard multi-class classification risk, based on the binary loss, is rarely directly minimized. This is due to (1) the lack of convexity and (2) the lack of smoothness (and even continuity). The classic approach consists in minimizing instead a convex surrogate. In this paper, we propose to replace the usually considered deterministic decision rule by a stochastic one, which allows obtaining a smooth risk (generalizing the expected binary loss, and more generally the cost-sensitive loss). Practically, this (empirical) risk is minimized by performing a gradient descent in the function space linearly spanned by a base learner (a.k.a. boosting). We provide a convergence analysis of the resulting algorithm and experiment it on a bunch of synthetic and real-world data sets (with noiseless and noisy domains, compared to convex and non-convex boosters). | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Multi-class classification |2 nationallicence | |
| 690 | 7 | |a Boosting |2 nationallicence | |
| 690 | 7 | |a Binary loss |2 nationallicence | |
| 690 | 7 | |a Noise-tolerant learning |2 nationallicence | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 305-332 |x 0885-6125 |q 100:2-3<305 |1 2015 |2 100 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5491-2 |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/s10994-015-5491-2 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 100 |E 1- |a Geist |D Matthieu |u IMS - MaLIS Research Group and UMI 2958 (GeorgiaTech-CNRS), CentraleSupélec, Metz, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 305-332 |x 0885-6125 |q 100:2-3<305 |1 2015 |2 100 |o 10994 | ||