Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multitask Learning of Vegetation Biochemistry from Hyperspectral Data   Statistical models have been successful in accurately estimating the
biochemical contents of vegetation from the reflectance spectra. However, their
performance deteriorates when there is a scarcity of sizable amount of ground
truth data for modeling the complex non-linear relationship occurring between
the spectrum and the biochemical quantity. We propose a novel Gaussian process
based multitask learning method for improving the prediction of a biochemical
through the transfer of knowledge from the learned models for predicting
related biochemicals. This method is most advantageous when there are few
ground truth data for the biochemical of interest, but plenty of ground truth
data for related biochemicals. The proposed multitask Gaussian process
hypothesizes that the inter-relationship between the biochemical quantities is
better modeled by using a combination of two or more covariance functions and
inter-task correlation matrices. In the experiments, our method outperformed
the current methods on two real-world datasets.

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The categories of this paper are: physics
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys