Results 1 to 10 of about 300,432 (267)

Semiparametric Preference Learning

Tsinghua Science and Technology, 2014
Unlike traditional supervised learning problems, preference learning learns from data available in the form of pairwise preference relations between instances. Existing preference learning methods are either parametric or nonparametric in nature.
Yi Zhen, Yangqiu Song, Dit-Yan Yeung
doaj   +1 more source

Prediction of the sodium absorption ratio using data-driven models: a case study in Iran

Geology, Ecology, and Landscapes, 2020
In this investigation, two data-driven models, i.e., Gaussian Process (GP) and Support Vector Machine (SVM), were used to predict the sodium absorption ratio (SAR) in three sub-watersheds (Khorramabad, Biranshahr, and Alashtar) in Iran.
Balraj Singh
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Revisiting statefinder via Gaussian process

European Physical Journal C: Particles and Fields
The statefinder diagnostic is useful to discriminate dark energy models. In this paper, under the minimum assumption of a spatially flat Friedmann–Lemaître–Robertson–Walker Universe, we reconstruct the statefinder pair $$\{r(z),s(z)\}$$ { r ( z ) , s ( z
Zhihua Feng, Lixin Xu
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Patterns and drivers of population trends on individual Breeding Bird Survey routes using spatially explicit models and route-level covariates

Avian Conservation and Ecology
Spatial patterns in population trends, particularly those at fine geographic scales, can help better understand the factors driving population change in North American birds.
Adam C Smith, Veronica I Aponte, Allison D Binley, Amelia R Cox, Lindsay Daly, Courtney Donkersteeg, Brandon P. M. Edwards, Willow English, Marie-Anne R Hudson, Kendall M Jefferys, Barry G Robinson, Christian Roy   +11 more
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Probabilistic prediction of geomagnetic storms and the Kp index

Journal of Space Weather and Space Climate, 2020
Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current
Chakraborty Shibaji, Morley Steven Karl
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Hierarchical Facial Age Estimation Using Gaussian Process Regression

IEEE Access, 2019
Automatic age estimation from facial images has attracted increasing attention due to its promising potential in real-life computer vision applications. However, due to uncontrollable environments, insufficient and incomplete training data, strong person-
Manisha M. Sawant, Kishor Bhurchandi
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Regression with Gaussian Processes [PDF]

, 1997
The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out ...
openaire   +1 more source

Long-Term Forecasting of Solar Irradiation in Riyadh, Saudi Arabia, Using Machine Learning Techniques

Big Data and Cognitive Computing
Forecasting of time series data presents some challenges because the data’s nature is complex and therefore difficult to accurately forecast. This study presents the design and development of a novel forecasting system that integrates efficient data ...
Khalil AlSharabi, Yasser Bin Salamah, Majid Aljalal, Akram M. Abdurraqeeb, Fahd A. Alturki   +4 more
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Spiked Dirichlet Process Priors for Gaussian Process Models

Journal of Probability and Statistics, 2010
We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates.
Terrance Savitsky, Marina Vannucci
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Landslide susceptibility mapping using various soft computing techniques (Case study: A part of Haraz Watershed) [PDF]

مدل‌سازی و مدیریت آب و خاک
IntroductionA landslide is one of the mass movements on the top surface of the earth. Landslides have resulted in notable injury and damage to human life and destroyed infrastructure and property.
Alireza Sepahvand, Nasrin Beiranvand
doaj   +1 more source