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Multi-output Nonparametric Regression

Multi-output Nonparametric Regression,10.1007/11595014_29,José M. Matías

Multi-output Nonparametric Regression   (Citations: 3)
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Several non-parametric regression methods with various dependent variables that are possibly related are explored. The techniques which produce the best results in the simulations are those which incorporate the observations of the other response variables in the estimator. Compared to analogous single-response techniques, this approach results in a significant reduction in the quadratic error in the response.
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    • ...The dataset is generated according to the following regression functions used in [6]:...

    Wei Zhanget al. Decremental multi-output least square SVR learning

    • ...Previous approaches usually tackle this problem by inferring the unknown outputs conditionally from the known outputs based on their joint predictive distribution [1], [2], [3], [4], [5]...
    • ...The Co-kriging algorithm [18] and its variations [5] are similar to the Gaussian process approaches but are formulated under a transductive setting (for every test input, the model has to be re-learned)...
    • ...[5] also uses the kernel density estimator to model the joint distribution of all inputs and outputs, but this approach is shown to have a poor performance in their tests...
    • ...[5] also considers using some outputs as extra inputs to predict other outputs...

    Chao Yuan. Conditional multi-output regression

    • ...Matías [43] employed nonparametric regression models to obtain a multiple-output model, whereas Alvarez and Lawrence [44] proposed a convolution method to obtain multiple-output regressors from a single Gaussian process...

    Alistair Shiltonet al. A Division Algebraic Framework for Multidimensional Support Vector Reg...

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