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There are 8624 results for: content related to: A Domain Adaptation SVM and a Circular Validation Strategy for Land-Cover Maps Updating

  1. You have free access to this content
    Front Matter

    Kernel Methods for Remote Sensing Data Analysis

    Gustavo Camps-Valls, Lorenzo Bruzzone, Pages: i–xxix, 2009

    Published Online : 4 NOV 2009, DOI: 10.1002/9780470748992.fmatter

  2. Mean Kernels for Semi-Supervised Remote Sensing Image Classification

    Kernel Methods for Remote Sensing Data Analysis

    Luis Gómez-Chova, Javier Calpe-Maravilla, Lorenzo Bruzzone, Gustavo Camps-Valls, Pages: 223–246, 2009

    Published Online : 4 NOV 2009, DOI: 10.1002/9780470748992.ch10

  3. Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 6, Issue 6, December 2013, Pages: 472–481, Shuichi Kawano

    Article first published online : 18 SEP 2013, DOI: 10.1002/sam.11204

  4. GBC: Gradient boosting consensus model for heterogeneous data

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 7, Issue 3, June 2014, Pages: 161–174, Xiaoxiao Shi, Jean-Francois Paiement, David Grangier and Philip S. Yu

    Article first published online : 22 MAY 2013, DOI: 10.1002/sam.11193

  5. On Training and Evaluation of SVM for Remote Sensing Applications

    Kernel Methods for Remote Sensing Data Analysis

    Gustavo Camps-Valls, Lorenzo Bruzzone, Pages: 85–109, 2009

    Published Online : 4 NOV 2009, DOI: 10.1002/9780470748992.ch4

  6. Learning under nonstationarity: covariate shift and class-balance change

    Wiley Interdisciplinary Reviews: Computational Statistics

    Volume 5, Issue 6, November/December 2013, Pages: 465–477, Masashi Sugiyama, Makoto Yamada and Marthinus Christoffel du Plessis

    Article first published online : 27 AUG 2013, DOI: 10.1002/wics.1275

  7. Multi-Temporal Image Classification with Kernels

    Kernel Methods for Remote Sensing Data Analysis

    Jordi Muñnoz-Marí, Luis Gómez-Chova, Manel Martínez-Ramón, José Luis Rojo-Álvarez, Javier Calpe-Maravilla, Gustavo Camps-Valls, Pages: 125–145, 2009

    Published Online : 4 NOV 2009, DOI: 10.1002/9780470748992.ch6

  8. On handling negative transfer and imbalanced distributions in multiple source transfer learning

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 7, Issue 4, August 2014, Pages: 254–271, Liang Ge, Jing Gao, Hung Ngo, Kang Li and Aidong Zhang

    Article first published online : 18 APR 2014, DOI: 10.1002/sam.11217

  9. Identifying composite crosscutting concerns through semi-supervised learning

    Software: Practice and Experience

    Volume 44, Issue 12, December 2014, Pages: 1525–1545, Jianlin Zhu, Jin Huang, Daicui Zhou, Federico Carminati, Guoping Zhang and Qiang He

    Article first published online : 11 NOV 2013, DOI: 10.1002/spe.2234

  10. Semi-Supervised Clustering in Functional Genomics

    Mathematical Analysis of Evolution, Information, and Complexity

    Johann M. Kraus, Günther Palm, Friedhelm Schwenker, Hans A. Kestler, Pages: 243–271, 2009

    Published Online : 21 AUG 2009, DOI: 10.1002/9783527628025.ch9

  11. Unlabelled extra data do not always mean extra performance for semi-supervised fault prediction

    Expert Systems

    Volume 26, Issue 5, November 2009, Pages: 458–471, Cagatay Catal and Banu Diri

    Article first published online : 21 OCT 2009, DOI: 10.1111/j.1468-0394.2009.00509.x

  12. Semi-supervised probabilistic sentiment analysis: Merging labeled sentences with unlabeled reviews to identify sentiment

    Proceedings of the American Society for Information Science and Technology

    Volume 50, Issue 1, 2013, Pages: 1–10, Andrew Yates, Nazli Goharian and Wai Gen Yee

    Article first published online : 8 MAY 2014, DOI: 10.1002/meet.14505001031

  13. Semi-Supervised Classification Using Pattern Clustering

    Semi-Supervised and Unsupervised Machine Learning: Novel Strategies

    Amparo Albalate, Wolfgang Minker, Pages: 127–181, 2013

    Published Online : 13 FEB 2013, DOI: 10.1002/9781118557693.ch4

  14. Exploring and inferring user–user pseudo-friendship for sentiment analysis with heterogeneous networks

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 7, Issue 4, August 2014, Pages: 308–321, Hongbo Deng, Jiawei Han, Hao Li, Heng Ji, Hongning Wang and Yue Lu

    Article first published online : 2 MAY 2014, DOI: 10.1002/sam.11223

  15. Exploring Co-training strategies for opinion detection

    Journal of the Association for Information Science and Technology

    Volume 65, Issue 10, October 2014, Pages: 2098–2110, Ning Yu

    Article first published online : 10 MAR 2014, DOI: 10.1002/asi.23111

  16. EXPLOITING SUBTREES IN AUTO-PARSED DATA TO IMPROVE DEPENDENCY PARSING

    Computational Intelligence

    Volume 28, Issue 3, August 2012, Pages: 426–451, Wenliang Chen, Jun’ichi Kazama, Kiyotaka Uchimoto and Kentaro Torisawa

    Article first published online : 19 JUN 2012, DOI: 10.1111/j.1467-8640.2012.00451.x

  17. Exploiting associations between word clusters and document classes for cross-domain text categorization

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 4, Issue 1, February 2011, Pages: 100–114, Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He, Yuhong Xiong and Zhongzhi Shi

    Article first published online : 30 NOV 2010, DOI: 10.1002/sam.10099

  18. A Survey of Discriminative Language Modeling Approaches for Large Vocabulary Continuous Speech Recognition

    Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods

    Joseph Keshet, Samy Bengio, Pages: 115–137, 2009

    Published Online : 14 JAN 2009, DOI: 10.1002/9780470742044.ch8

  19. You have free access to this content
    Index

    Kernel Methods for Remote Sensing Data Analysis

    Gustavo Camps-Valls, Lorenzo Bruzzone, Pages: 401–403, 2009

    Published Online : 4 NOV 2009, DOI: 10.1002/9780470748992.index

  20. Semi-supervised classification of vegetation: preserving the good old units and searching for new ones

    Journal of Vegetation Science

    Volume 25, Issue 6, November 2014, Pages: 1504–1512, Lubomír Tichý, Milan Chytrý and Zoltán Botta-Dukát

    Article first published online : 5 MAY 2014, DOI: 10.1111/jvs.12193