Results 11 to 20 of about 195,855 (264)
Imitation learning with non-parametric regression [PDF]
Humans are very fast learners. Yet, we rarely learn a task completely from scratch. Instead, we usually start with a rough approximation of the desired behavior and take the learning from there. In this paper, we use imitation to quickly generate a rough solution to a robotic task from demonstrations, supplied as a collection of state-space ...
Vaandrager, Maarten +3 more
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Probability and Inferential Statistics
Application of statistical tools is essential for appropriate understanding of the collected data in clinical trials. The types of variables for the study are also decided in advance and the relevant statistical tools identified in the planning stage of ...
Rakesh Garg +3 more
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Back Analysis Algorithm of Soil Parameter Based on Non-parametric Regression [PDF]
Because of the uncertainty factors such as test condition,the variability of soil properties,soil sampling methods,sample disturbance and so on,test results of soil parameters obtained by field test have the discreteness.And results obtained by ...
CAO Jing,SUN Changning,ZHANG Ruimei,SONG Zhigang,LIU Haiming
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A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. An accurate model for forecasting demographic movements is important for decision making in social welfare policies and resource ...
Ka Kin Lam, Bo Wang
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Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures.
Granville J. Matheson +7 more
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Accurate transient stability assessment (TSA) and effective preventive control are important for the stable operation of power systems. With the superiorities in precision and efficiency, data-driven methods are widely used in TSA nowadays.
Yiwei Fu +5 more
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A Novel EM-Type Algorithm to Estimate Semi-Parametric Mixtures of Partially Linear Models
Semi- and non-parametric mixture of normal regression models are a flexible class of mixture of regression models. These models assume that the component mixing proportions, regression functions and/or variances are non-parametric functions of the ...
Sphiwe B. Skhosana +2 more
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Non-Parametric Calibration of Probabilistic Regression
The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has been investigated thoroughly in classification, it has not yet been well-established for regression tasks.
Hao Song 0007 +2 more
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Estatística aplicada à química: dez dúvidas comuns
Ten common doubts of chemistry students and professionals about their statistical applications are discussed. The use of the N-1 denominator instead of N is described for the standard deviation.
Livia Maria Zambrozi Garcia Passari +3 more
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Local Dimensionality Reduction for Non-Parametric Regression [PDF]
Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many local models need to be maintained simultaneously.
Heiko Hoffmann +2 more
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