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  • 1
    Electronic Resource
    Electronic Resource
    Locally Weighted Learning (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 11-73 
    Details
    ISSN: 1573-7462
    Keywords: locally weighted regression ; LOESS ; LWR ; lazy learning ; memory-based learning ; least commitment learning ; distance functions ; smoothing parameters ; weighting functions ; global tuning ; local tuning ; interference
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006559212014
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Locally Weighted Learning for Control (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 75-113 
    Details
    ISSN: 1573-7462
    Keywords: locally weighted regression ; LOESS ; LWR ; lazy learning ; memory-based learning ; least commitment learning ; forward models ; inverse models ; linear quadratic regulation (LQR) ; shifting setpoint algorithm ; dynamic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006511328852
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  • 3
    Electronic Resource
    Electronic Resource
    The Racing Algorithm: Model Selection for Lazy Learners (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 193-225 
    Details
    ISSN: 1573-7462
    Keywords: lazy learning ; model selection ; cross validation ; optimization ; attribute selection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Given a set of models and some training data, we would like to find the model that best describes the data. Finding the model with the lowest generalization error is a computationally expensive process, especially if the number of testing points is high or if the number of models is large. Optimization techniques such as hill climbing or genetic algorithms are helpful but can end up with a model that is arbitrarily worse than the best one or cannot be used because there is no distance metric on the space of discrete models. In this paper we develop a technique called “racing” that tests the set of models in parallel, quickly discards those models that are clearly inferior and concentrates the computational effort on differentiating among the better models. Racing is especially suitable for selecting among lazy learners since training requires negligible expense, and incremental testing using leave-one-out cross validation is efficient. We use racing to select among various lazy learning algorithms and to find relevant features in applications ranging from robot juggling to lesion detection in MRI scans.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006556606079
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