Results 221 to 230 of about 65,050 (262)

A database for automatic classification of forest species

Machine Vision and Applications, 2012
Forest species can be taxonomically divided into groups, genera, and families. This is very important for an automatic forest species classification system, in order to avoid possible confusion between species belonging to two different groups, genera, or families.
Jefferson G. Martins, Luiz E. Soares de Oliveira, Silvana Nisgoski, Robert Sabourin   +3 more
openaire   +1 more source

Tailoring Random Forest for Requirements Classification

2020
Automated and semi-automated classifications of requirements (type and topics) are important for making requirements management more efficient. We report how we tailored a random forest approach in the EU funded project OpenReq, aiming for sufficient quality for practical use in bid projects. Evaluation with thirty thousand requirements in English from
Andreas A. Falkner, Gottfried Schenner, Alexander Schörghuber   +2 more
openaire   +1 more source

Ontological Random Forests for Image Classification

International Journal of Information Retrieval Research, 2015
Previous image classification approaches mostly neglect semantics, which has two major limitations. First, categories are simply treated independently while in fact they have semantic overlaps. For example, “sedan” is a specific kind of “car”. Therefore, it's unreasonable to train a classifier to distinguish between “sedan” and “car”.
Ning Xu 0007, Jiangping Wang, Guojun Qi, Thomas S. Huang, Weiyao Lin   +4 more
openaire   +1 more source

Variation and classification of forest

1981
Serai and swamp communities such as those mentioned above were named and briefly described by Chipp (1927) and Taylor (1960). Such small-scale local variation is relatively easy to recognise and explain. Even the earliest forest investigations, however, revealed the existence of differences from one part of the forest zone to another in the structure ...
J. B. Hall, M. D. Swaine
openaire   +1 more source

Hyperspectral feature selection for forest classification

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004
Hyperspectral imagery contains many correlated bands, which not only increase computing complexity but also degrade classification accuracy if not enough training data are available [David A. Landgrebe, 2003]. To mitigate this problem, linear image transformations, such as principle components analysis (PCA), minimum noise fraction (MNF), and canonical
Tian Han 0003, David G. Goodenough, Andrew Dyk, Hao Chen 0004   +3 more
openaire   +1 more source

Processing hyperion and ali for forest classification

IEEE Transactions on Geoscience and Remote Sensing, 2003
Hyperion (a hyperspectral sensor) and the Advanced Land Imager (ALI) (a multispectral sensor) are carried on the National Aeronautics and Space Administration's Earth Observing 1 (EO-1) satellite. The Evaluation and Validation of EO-1 for Sustainable Development (EVEOSD) is our project supporting the EO-1 mission.
David G. Goodenough, Andrew Dyk, K. Olaf Niemann, Jay S. Pearlman, Hao Chen 0004, Tian Han 0003, Matthew Murdoch, Chris West   +7 more
openaire   +1 more source

Random Forests for land cover classification

Pattern Recognition Letters, 2006
Random Forests are considered for classification of multisource remote sensing and geographic data. Various ensemble classification methods have been proposed in recent years. These methods have been proven to improve classification accuracy considerably. The most widely used ensemble methods are boosting and bagging.
Pall Oskar Gislason, Jón Atli Benediktsson, Johannes R. Sveinsson   +2 more
openaire   +1 more source

Classification Using Probabilistic Random Forest

2015 IEEE Symposium Series on Computational Intelligence, 2015
The Probabilistic random forest is a classification model which chooses a subset of features for each random forest depending on the F-score of the features. In other words, the probability of a feature being chosen in the feature subset increases as the F-score of the feature in the dataset.
Rajhans Gondane, V. Susheela Devi
openaire   +1 more source

Classification of cardiotocography records by random forest

2013 36th International Conference on Telecommunications and Signal Processing (TSP), 2013
The cardiotocography (CTG) is a diagnostic method which is widely used in prenatal care. The CTG is indicated since 27 weeks of pregnancy and it measures heart activity, uterine contraction and fetal movement. Results of the CTG allow recognizing of three basic different fetal states (physiological, suspect and pathological) and an obstetrician can ...
Tomas Peterek, Jana Krohova, Pavel Dohnálek, Petr Gajdos   +3 more
openaire   +1 more source

Classification Using Streaming Random Forests

IEEE Transactions on Knowledge and Data Engineering, 2011
We consider the problem of data stream classification, where the data arrive in a conceptually infinite stream, and the opportunity to examine each record is brief. We introduce a stream classification algorithm that is online, running in amortized O(1) time, able to handle intermittent arrival of labeled records, and able to adjust its parameters to ...
Hanady M. Abdulsalam, David B. Skillicorn, Patrick Martin 0001   +2 more
openaire   +1 more source