[PDF] Top 20 Gaussian Processes for Ordinal Regression
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Gaussian Processes for Ordinal Regression
... Gaussian processes (O’Hagan, 1978; Neal, 1997) have provided a promising non-parametric Bayesian approach to metric regression (Williams and Rasmussen, 1996) and classification prob- lems (Williams ... See full document
23
Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... specifically Gaussian Processes (GPs) or relevance vector machines (RVMs) is becoming very popular and attractive due to incorporation of uncertainty and the bypassing of unattractive features from methods ... See full document
13
Statistical methods for critical scenarios in aeronautics
... In the rest of the section, let us comment on different strategies which could have been used to incorporate time correlation effects using gaussian processes approach in our regression model. A ... See full document
14
Deep Gaussian Processes
... Although the experiments presented here considered only up to 5 layers in the hierarchy, the methodology is directly applicable to deeper architectures, with which we intend to experiment in the future. The ... See full document
9
Efficient modeling of latent information in supervised learning using Gaussian processes
... regression on all the data from 10 different road and tyre conditions. (c) The top plot visualizes the fitted model with respect to one of the conditions in the training data and the bottom plot shows the ... See full document
9
Forecasting modeling with kernel function integration in gaussian processes
... solving Regression, Classification, and Decision – typed ...for regression (Williams and Rasmussen, 2006). In machine learning, Gaussian process has advantage over other techniques because its ... See full document
6
Gaussian Processes for Blazar Variability Studies
... PKS 1502+106 has shown an isolated outburst visible across the electromagnetic spectrum. The results of the GP regression are visible in the right panels of Fig. 2. The amplitudes at each of our radio frequencies ... See full document
7
Detecting periodicities with Gaussian processes
... We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples. Our approach is based on Gaussian process ... See full document
19
String and Membrane Gaussian Processes
... In regression problems, examples include the Bayesian Committee Machines (BCM) of Tresp (2000), the generalized product of experts (gPoE) model of Cao and Fleet (2014), and the robust Bayesian Committee Machines ... See full document
87
Deep Ordinal Regression for Pledge Specificity Prediction
... the ordinal classes Y ...q. Gaussian uses a different mech- anism, as described in Section ...a Gaussian histogram density centred at µ = y, discretised by way of integration of the PDF between ... See full document
12
Gaussian processes for computer experiments
... practice, Gaussian process-based prediction of unknown functions is significantly less understood than other standard techniques for function ...nearest-neighboor regression or kernel smoothing, can ... See full document
17
Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes
... In this section, we compare patched GP with the Gaussian Markov random field approach to the GP regression (Lindgren et al., 2011, GMRF), which was reported to scale great with massive data set; we ... See full document
29
Wind turbine rotor acceleration : identification using Gaussian regression
... Abstract: Gaussian processes prior model methods for data analysis are applied to wind turbine time series data to identify both rotor speed and rotor acceleration from a poor measurement of rotor ...of ... See full document
8
Extremes of threshold-dependent Gaussian processes
... result in (2.10) in some cases allows for replacement of T by ∞ . In this case, Theorem 2.3 can be applied directly for the asymptotics of the tail probability of maximum over infinite-time horizon of Gaussian ... See full document
35
Sparse Spectrum Gaussian Process Regression
... We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regres- sion ... See full document
17
Compressed Gaussian Process for Manifold Regression
... As a default in these analyses, we use m = 60, which seems to be a reasonable choice of upper bound for the dimension of the linear subspace to compress to. In addition, we implement two stage GP (2GP) where the ... See full document
26
GPstuff: Bayesian Modeling with Gaussian Processes
... Gaussian process (GP) prior provides a flexible building block for many hierarchical Bayesian mod- els (Rasmussen and Williams, 2006). GPstuff (v4.1) is a versatile collection of computational tools for GP models ... See full document
5
Optimality of Poisson Processes Intensity Learning with Gaussian Processes
... The aim of this paper is to advance the theoretical understanding of the method of Adams et al. (2009), which they termed “Sigmoidal Gaussian Cox Process” (SGCP). It is by now well known both from theory and ... See full document
11
Ordinal Logistic Regression for the Estimate of the Response Functions in the Conjoint Analysis
... In the Conjoint Analysis (COA) model proposed here—a new approach to estimate more than one response function— an extension of the traditional COA, the polytomous response variable (i.e. evaluation of the overall ... See full document
7
Depression Among Pregnant Latinas In South Carolina
... The effect of predictors on the response may be linear or non-linear, of much or little significance, and at times magnified with interactions. When such complicated relationships exist, models that use ensemble of trees ... See full document
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