| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000329786 |
| 005 | | 20241016151514 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438168640 |
| 035 | |
▼a (MiAaPQ)AAI10823049 |
| 035 | |
▼a (MiAaPQ)umn:19188 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Mithal, Varun. |
| 245 | 10 |
▼a Computational Techniques to Identify Rare Events in Spatio-Temporal Data. |
| 260 | |
▼a [S.l.] :
▼b University of Minnesota.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 109 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Vipin Kumar. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2018. |
| 520 | |
▼a Recent attention on the potential impacts of land cover changes to the environment as well as long-term climate change has increased the focus on automated tools for global-scale land surface monitoring. Advancements in remote sensing and data c |
| 520 | |
▼a We study the problem of identifying land cover changes such as forest fires as a supervised binary classification task with the following characteristics: (i) instead of true labels only imperfect labels are available for training samples. These |
| 520 | |
▼a We explore approaches to reduce errors in remote sensing based classification products, which are common due to poor data quality (eg., instrument failure, atmospheric interference) as well as limitations of the classification models. We present |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Minnesota.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0130 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998538
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |