標(biāo)題: Titlebook: Machine Learning for Ecology and Sustainable Natural Resource Management; Grant Humphries,Dawn R. Magness,Falk Huettmann Book 2018 Springe [打印本頁] 作者: indulge 時間: 2025-3-21 19:02
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作者: Clumsy 時間: 2025-3-21 23:25 作者: insightful 時間: 2025-3-22 03:50
Grant R. W. Humphries,Falk Huettmannere Aufmerksamkeit gewidmet...Das Lexikon der Informatik ist für jeden, der sich in die Welt der Informatik begrifflich sicher und kompetent bewegen will, ein unverzichtbarer Begleiter...Der Schwerpunkt der überarbeitung zur 14. Auflage lag auf dem Gebiet der Datensicherheit..978-3-540-72550-3作者: 魯莽 時間: 2025-3-22 06:40 作者: 小畫像 時間: 2025-3-22 10:17 作者: Anthology 時間: 2025-3-22 15:38 作者: 無所不知 時間: 2025-3-22 21:02 作者: 并排上下 時間: 2025-3-22 23:37
From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared any contractors, governments, a lifestyle and subsequent belief system and society. However, the methodology employed in traditional statistical analyses are well-published and known to violate many of their required statistical assumptions to allow for valid inferences. Often, this topic becomes th作者: 打擊 時間: 2025-3-23 03:41 作者: 哀求 時間: 2025-3-23 06:20 作者: 縱欲 時間: 2025-3-23 12:48
Landscape Applications of Machine Learning: Comparing Random Forests and Logistic Regression in Multe conifer forest. Visual inspection of the predicted occurrence probability maps shows that random forest produces predictions that are more discriminatory, with higher range of predicted probability and higher spatial heterogeneity than logistic regression. The logistic regression model has an AUC 作者: 哀求 時間: 2025-3-23 17:24 作者: conscience 時間: 2025-3-23 18:21 作者: nepotism 時間: 2025-3-24 01:09 作者: Notify 時間: 2025-3-24 02:24
Breaking Away from ‘Traditional’ Uses of Machine Learning: A Case Study Linking Sooty Shearwaters (, correlation of r?>?0.8 for SOI values from 0 to 14?months after peak chick size. A combination of parameters and regions best explain the variation in the SOI data, however the most important variables are those that represent general turbulence in the sub-Antarctic water and Polar front regions (i作者: 冷峻 時間: 2025-3-24 08:19 作者: 抗生素 時間: 2025-3-24 13:48
Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (,) Vocaliz handling. We found that random forests from the h2o and ‘randomForest’ packages in R performed best with regards to accuracy, ‘randomForest’ and ‘gbm’ performing best with regards to speed, and ‘tensor forest’ and ‘h2o’ implementations performing best with regards to memory. Furthermore, we were ab作者: macrophage 時間: 2025-3-24 14:58 作者: Minuet 時間: 2025-3-24 19:42 作者: 刺激 時間: 2025-3-25 02:33 作者: Magnificent 時間: 2025-3-25 04:33 作者: Insatiable 時間: 2025-3-25 10:37
Brian D. Young,John Yarie,David Verbyla,Falk Huettmann,F. Stuart Chapin III作者: 變色龍 時間: 2025-3-25 14:04
Grant R. W. Humphries,Rachel T. Buxton,Ian L. Jones作者: arbiter 時間: 2025-3-25 18:41
Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Syntitative reasoning. It allows for relevant progress, all while the global environmental state decays further, climate change remain unaccounted for and sustainability policies remain outdated urging for an effective change of global culture and governance.作者: faucet 時間: 2025-3-25 21:52 作者: Legend 時間: 2025-3-26 01:32 作者: 猜忌 時間: 2025-3-26 05:27 作者: 加強(qiáng)防衛(wèi) 時間: 2025-3-26 09:01
Book 2018 from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite a作者: 商議 時間: 2025-3-26 14:48 作者: BROOK 時間: 2025-3-26 17:03
iffserkl?rungen.F?rderung der begrifflichen Vernetzung durch.Begriffskompetenz in der Informatik: Das Lexikon der Informatik vermittelt die heute notwendige Sicherheit im Umgang mit der Begriffswelt der Informatik. Die Auswahl der über 6000 Kurzdefinitionen unter mehr als 5000 Stichworten ist repr?s作者: 植物茂盛 時間: 2025-3-26 23:10 作者: transient-pain 時間: 2025-3-27 03:25 作者: 使害怕 時間: 2025-3-27 05:30 作者: Cervical-Spine 時間: 2025-3-27 10:59
Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Sresting uses?of these sophisticated algorithms which are driving inference and understanding in natural resource management. The concept behind machine learning is to provide data to a computer and allow the machine to ‘learn’ the patterns in those data. These learned relationships are applied and a作者: bronchiole 時間: 2025-3-27 14:42 作者: Liberate 時間: 2025-3-27 20:18 作者: 苦澀 時間: 2025-3-28 00:00
From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared over several hundred years (without computers), and it is usually centered around frequency mindsets and central theorems, summarized by Zar (.). Nowadays, statistics are easily done with a computer and the internet, which brings forward new approaches to analysis and inference. Traditional (freque作者: puzzle 時間: 2025-3-28 04:21
Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methodsof their strengths and weaknesses in applied contexts. Tree-based methods such as Random Forests (RF) and Boosted Regression Trees (BRT) are powerful ML approaches that make no assumptions about the functional forms of the relationship with predictors, are flexible in handling missing data, and can 作者: exostosis 時間: 2025-3-28 09:16
Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Tlethora of techniques based on ensemble methods. In this chapter, I explore techniques relevant to macroscale ecological niche modelling in a regression context. I evaluate the challenges while predicting suitable habitats under future climates, and address issues related to high dimensional data li作者: 光明正大 時間: 2025-3-28 13:22 作者: SEEK 時間: 2025-3-28 17:25
‘Batteries’ in Machine Learning: A First Experimental Assessment of Inference for Siberian Crane Brec, and two subpopulations are known. Here we present for the first time a machine learning-based summer habitat analysis using nesting data for the eastern population in the breeding grounds employing predictive modeling with 74 GIS predictors. There is a typical desire for parsimony to help increas作者: Picks-Disease 時間: 2025-3-28 18:55
Landscape Applications of Machine Learning: Comparing Random Forests and Logistic Regression in Multmentation. Our goal was to compoare logistic regression and random forest in multi-scale optimized predictive model of occurrence of the American marten (.) in northern Idaho USA. There have been relatively few formal comparisons of the performance of multi-scale modeling between logistic regression作者: febrile 時間: 2025-3-29 00:45
Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case s. Machine-learning based ecological niche models that account for landscape characteristics and changes in climate have been effective tools for deciphering patterns in messy, presence-only datasets, and predicting shifts in wildlife distributions over time. Bioclimatic niche models are sometimes c作者: rods366 時間: 2025-3-29 06:17
Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machinend temporal scale these ‘climate-scapes’ are often less studied, are poorly understood and assessments are lacking. The accuracy of climate-scapes is often affected by local topography and wider couplings. The science of local climate-scapes is still in its infancy, so are the methods of inquiry and作者: cataract 時間: 2025-3-29 10:25
Using TreeNet, a Machine Learning Approach to Better Understand Factors that Influence Elevated Blooiated with exposure are often complex and difficult to assess. Machine learning models are suitable for prediction and for gaining biologically meaningful insight into the potential impacts of Pb on wildlife populations. However, despite their potential, they are often under-utilized in the field of作者: Solace 時間: 2025-3-29 15:24 作者: Glower 時間: 2025-3-29 15:37
Image Recognition in Wildlife Applications the images delivered to our inboxes, are widely available (O’Connell AF, Nichols JD, Ullas Karanth K Camera traps in animal ecology: methods and analyses. Book, Whole. Springer Science & Business Media, 2010). Ecologists and wildlife biologists are also deploying camera and videography equipment as作者: Ambiguous 時間: 2025-3-29 23:06
Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (,) Vocaliznd North Pacific. Although some mixing occurs during the non-breeding season, genetic evidence demonstrates that these populations are diverging. However, genetic information for the study of phylogenetics can be costly and time-consuming to obtain. Vocalizations could offer a more cost-effective wa作者: aerobic 時間: 2025-3-30 01:08
https://doi.org/10.1007/978-3-319-96978-7Quantitative ecology; artificial intelligence; Statistics; data mining; machine learning; Wildlife biolog作者: URN 時間: 2025-3-30 08:05 作者: 易于 時間: 2025-3-30 08:46
Grant Humphries,Dawn R. Magness,Falk HuettmannShows ecologists cutting-edge methods that can help in understanding complex systems with multiple interacting variablesto and to form predictive hypotheses from large datasets.Provides practical exam作者: 符合你規(guī)定 時間: 2025-3-30 13:32 作者: 自愛 時間: 2025-3-30 20:33 作者: commonsense 時間: 2025-3-30 21:24
