Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Initialising Kernel Adaptive Filters via Probabilistic Inference   We present a probabilistic framework for both (i) determining the initial
settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive
KAFs whereby in addition to weights and dictionaries, kernel parameters are
learnt sequentially. This is achieved by formulating the estimator as a
probabilistic model and defining dedicated prior distributions over the kernel
parameters, weights and dictionary, enforcing desired properties such as
sparsity. The model can then be trained using a subset of data to initialise
standard KAFs or updated sequentially each time a new observation becomes
available. Due to the nonlinear/non-Gaussian properties of the model, learning
and inference is achieved using gradient-based maximum-a-posteriori
optimisation and Markov chain Monte Carlo methods, and can be confidently used
to compute predictions. The proposed framework was validated on nonlinear time
series of both synthetic and real-world nature, where it outperformed standard
KAFs in terms of mean square error and the sparsity of the learnt dictionaries.

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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex