| LDR | | 02136nam u200421 4500 |
| 001 | | 000000419571 |
| 005 | | 20190215163801 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438165601 |
| 035 | |
▼a (MiAaPQ)AAI10906465 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Chakrabarti, Subit. |
| 245 | 10 |
▼a Machine Learning Algorithms for Spatio-Temporal Scaling of Remotely Sensed Data. |
| 260 | |
▼a [S.l.]:
▼b University of Florida.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 181 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Florida, 2017. |
| 520 | |
▼a Over the last decade, machine learning algorithms have been extensively studied for their ability to estimate non-linear and dynamic mappings among sets of variables with high accuracies, even for large datasets. With satellite sensors providing |
| 520 | |
▼a This dissertation develops novel machine learning algorithms to downscale RS observations of soil moisture (SM) and microwave brightness temperatures (TB) utilizing spatial and temporal correlations among other disparate RS and in-situ datasets. |
| 520 | |
▼a The algorithms use kernel methods, information theoretic learning and ensemble learning to provide estimates of high resolution SM and TB satisfying different conditions of variable complexities, data availabilities and expected computational pe |
| 590 | |
▼a School code: 0070. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Hydrologic sciences. |
| 650 | 4 |
▼a Remote sensing. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0388 |
| 690 | |
▼a 0799 |
| 710 | 20 |
▼a University of Florida.
▼b Electrical and Computer Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0070 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000763
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |