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Showing 1 to 10 of 10 for “"Non-stationary Environments"”.

  1. Learning in non-stationary Environments

    … domain is assumed to be the same, denoted as a stationary environment. If this is not the case and the distributions change between the two domains, it is called a non-stationary environment. <br /><br /> The research area of Domain Adaptation offers methods to adapt the input data or an already …

    bielefeld Repository record for Learning in non-stationary Environments (opens in a new tab)

  2. Causal Inference: Heterogeneous Effects and Non-stationary Environments

    … the problem of estimating treatment effects in non-stationary data. In this setting, using old data to make inferences can lead to unreliable results. We propose a novel procedure that helps smooth out the data non-stationarity by providing a way to resample previous data to match the …

    mit Repository record for Causal Inference: Heterogeneous Effects and Non-stationary Environments (opens in a new tab)

  3. Learning with high dimensional data and preprocessing in non-stationary environments

    … the Random Projection technique is analyzed in non-stationary envi- ronments. It is shown, that the Johnson-Lindenstrauss Lemma also holds for stream classification tasks. Further, performance comparisons of different classifiers on the projected and the orig- inal space are provided, and it is …

    bielefeld Repository record for Learning with high dimensional data and preprocessing in non-stationary environments (opens in a new tab)

  4. An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning

    … using an ensemble of classifiers, Learn++.NSE (Non-Stationary Environments), specifically for the case where the nature of knowledge to be learned is evolving. Learn++.NSE is a novel approach to evaluating and organizing existing knowledge (classifiers) according to the most recent data …

    rowan Repository record for An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning (opens in a new tab)

  5. Dynamically Evaluated Gravity Compensation for the RAVEN Surgical Robot

    … re-orientable bases or for those operating in non-stationary environments such as boats, space stations, or moving vehicles.

    washington Repository record for Dynamically Evaluated Gravity Compensation for the RAVEN Surgical Robot (opens in a new tab)

  6. Neural Encoding of Prior Experience in Sensorimotor Behavior

    … Third, I demonstrate that the results hold in non-stationary environments when animals adapt to new temporal statistics. Fourth, I present a computational model that recapitulates the behavioral and neural findings and provides a solution for incorporating temporal expectations in neural …

    mit Repository record for Neural Encoding of Prior Experience in Sensorimotor Behavior (opens in a new tab)

  7. Theory and application of learning automata.

    … of these automata has been measured in stationary and non-stationary environments. The operation of a hierarchical automaton controlling the memory size of a Tsetlin automaton is also investigated. Two new automata are proposed with the aim of avoiding the operational disadvantages of …

    rgu Repository record for Theory and application of learning automata. (opens in a new tab)

  8. On upper confidence bound algorithms for piecewise-stationary stochastic multi-armed bandits and the variants

    … bound. Original MAB problems are considered in a stationary environment, where the reward distributions do not evolve over time. Many real-world applications, however, have a non-stationary nature that cannot be fully characterized by the stationary settings. In this thesis, we mainly study the …

    uiuc Repository record for On upper confidence bound algorithms for piecewise-stationary stochastic multi-armed bandits and the variants (opens in a new tab)

  9. Knowledge-Based Architecture for Integrated Condition Based Maintenance of Engineering Systems

    … feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.

    gatech Repository record for Knowledge-Based Architecture for Integrated Condition Based Maintenance of Engineering Systems (opens in a new tab)