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Selection: with tag support-vector-machines [4 articles] 

 

Statistical modeling: the two cultures (with comments and a rejoinder by the author)

  
Statistical Science, Vol. 16, No. 3. (August 2001), pp. 199-231, https://doi.org/10.1214/ss/1009213726

Abstract

There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 39

  
(February 2014)
Keywords: inrmm-list-of-tags   stakeholder-and-community-participation   stand-composition   stand-density   stand-structure   standard   standardized-precipitation-evapotranspiration-index   standardized-precipitation-index   staphylea-colchica   staphylea-pinnata   staphylococcus-aureus   state-shift   stationarity   statistical-downscaling   statistics   stegophora-ulmea   stem-canker   stem-rot   stem-rust   stepping-stones   sterculia-foetida   sterculia-urens   sterilization   sternochetus-mangiferae   stigmella-spp   stochastic-dynamic-programming   stochastic-state-transition   stomatal-conductance   stone-weierstrass-theorem   stoniness   storage   storm   storm-intensity   strategy   strategy-vs-tactic   stratification   string-instrument   strix-uralensis   strobus   strom   strophosoma-melanogrammus   structure   stryphnodendron-microstachyum   stunting   subalpine   subalpine-belt   subtropical-areas   subtropical-climate   subtropical-forest   subtropical-forests   subtropical-mountain-system   succession   succession-pathways   sudden-changes   sudden-oak-death   sudden-transition   sudden-transitions   sulphates   sulphur   sumava-national-park   sun   super-derecho   super-terminal-speed   supervised-training   supply-chain   support-vector-machines   supporting-services   surface-roughness   surprise   survey   survival   sus-scrofa   susceptibility   sustainability   sustainable-development   sustainable-forest   sustainable-forest-management   sustainable-forestry   sweden   swietenia-macrophylla   swiss   switzerland   syagrus-romanzoffiana   sycamore   symbiosis   symphoricarpos-albus   symphoricarpos-spp   symphytum-tauricum   symptoms   synanthropic-species   synergy   synonyms   syntax-vs-semantics   syntaxonomy   system   system-catastrophe   system-dynamics   system-engineering   system-of-systems   system-theory  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

 

Modelling the spatial distribution of tree species with fragmented populations from abundance data

  
Community Ecology, Vol. 10, No. 2. (1 December 2009), pp. 215-224, https://doi.org/10.1556/comec.10.2009.2.12

Abstract

Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species ( Quercus suber ) ...

 

Spatial prediction models for landslide hazards: review, comparison and evaluation

  
Natural Hazards and Earth System Science, Vol. 5, No. 6. (7 November 2005), pp. 853-862, https://doi.org/10.5194/nhess-5-853-2005

Abstract

The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting "present" and "future" landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/support-vector-machines

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.