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CSDA
2007
102views more  CSDA 2007»
14 years 9 months ago
Smooth functions and local extreme values
Given a sample of n observations y1, . . . , yn at time points t1, . . . , tn we consider the problem of specifying a function ˜f such that ˜f • is smooth, • fits the data ...
A. Kovac
85
Voted
SAC
2008
ACM
14 years 9 months ago
Local linear regression with adaptive orthogonal fitting for the wind power application
For short-term forecasting of wind generation, a necessary step is to model the function for the conversion of meteorological variables (mainly wind speed) to power production. Su...
Pierre Pinson, Henrik Aalborg Nielsen, Henrik Mads...
TNN
2011
132views more  TNN 2011»
14 years 4 months ago
Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals
—Prediction intervals (PIs) have been proposed in the literature to provide more information by quantifying the level of uncertainty associated to the point forecasts. Traditiona...
Abbas Khosravi, Saeid Nahavandi, Douglas C. Creigh...
MA
2010
Springer
114views Communications» more  MA 2010»
14 years 8 months ago
Finite-sample inference with monotone incomplete multivariate normal data, II
We continue our recent work on finite-sample, i.e., non-asymptotic, inference with two-step, monotone incomplete data from Nd(µ, Σ), a multivariate normal population with mean ...
Wan-Ying Chang, Donald St. P. Richards
ICCV
2007
IEEE
15 years 11 months ago
Active Learning with Gaussian Processes for Object Categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gauss...
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, Tr...