@CONFERENCE\{IMM2010-05997,
    author       = "J. Arenas-Garķa and C. Moriana-Varo and J. Larsen",
    title        = "Combination of supervised and semi-supervised regression models for improved unbiased estimation",
    year         = "2010",
    month        = "sep",
    keywords     = "semi-supervised learning",
    pages        = "364 - 368",
    booktitle    = "Proceedings of the Seventh International Symposium on Wireless Communication Systems (ISWCS2010)",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "{IEEE} Press",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5997-full.html",
    abstract     = "In this paper we investigate the steady-state performance
of semisupervised regression models adjusted using a
modified {RLS-}like algorithm, identifying the situations where the
new algorithm is expected to outperform standard {RLS}. By using
an adaptive combination of the supervised and semisupervised
methods, the resulting adaptive filter is guaranteed to perform
at least as well as the best contributing filter, therefore achieving
universal performance. The analysis and behavior of the methods
is illustrated through a set of examples in a plant identification
setup, analyzing both steady-state and convergence situations.",
    isbn_issn    = "DOI:10.1109/ISWCS.2010.5624325"
}