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  • lazy learning  (3)
  • locally weighted regression  (2)
  • reinforcement learning  (2)
  • 1
    Digitale Medien
    Digitale Medien
    Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time (1993)
    Springer
    Machine learning 13 (1993), S. 103-130 
    Details
    ISSN: 0885-6125
    Schlagwort(e): Memory-based learning ; learning control ; reinforcement learning ; temporal differencing ; asynchronous dynamic programming ; heuristic search ; prioritized sweeping
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We present a new algorithm, prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems. It successfully solves large state-space real-time problems with which other methods have difficulty.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1023/A:1022635613229
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  • 2
    Digitale Medien
    Digitale Medien
    Prioritized sweeping: Reinforcement learning with less data and less time (1993)
    Springer
    Machine learning 13 (1993), S. 103-130 
    Details
    ISSN: 0885-6125
    Schlagwort(e): Memory-based learning ; learning control ; reinforcement learning ; temporal differencing ; asynchronous dynamic programming ; heuristic search ; prioritized sweeping
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We present a new algorithm,prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems. It successfully solves large state-space real-time problems with which other methods have difficulty.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1007/BF00993104
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Artikel (Nationallizenzen)
    Volltext
  • 3
    Digitale Medien
    Digitale Medien
    The Racing Algorithm: Model Selection for Lazy Learners (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 193-225 
    Details
    ISSN: 1573-7462
    Schlagwort(e): lazy learning ; model selection ; cross validation ; optimization ; attribute selection
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: 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.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1023/A:1006556606079
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  • 4
    Digitale Medien
    Digitale Medien
    Locally Weighted Learning for Control (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 75-113 
    Details
    ISSN: 1573-7462
    Schlagwort(e): 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
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: 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.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1023/A:1006511328852
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  • 5
    Digitale Medien
    Digitale Medien
    Locally Weighted Learning (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 11-73 
    Details
    ISSN: 1573-7462
    Schlagwort(e): locally weighted regression ; LOESS ; LWR ; lazy learning ; memory-based learning ; least commitment learning ; distance functions ; smoothing parameters ; weighting functions ; global tuning ; local tuning ; interference
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: 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.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1023/A:1006559212014
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