Results 31 to 40 of about 921,406 (312)
Action classification using a discriminative non-parametric hidden Markov model [PDF]
, 2013 We classify human actions occurring in videos, using the skeletal joint positions extracted from a depth image sequence as features. Each action class is represented by a non-parametric Hidden Markov Model (NP-HMM) and the model parameters are learnt in ...
Maybank, Stephen J. +5 more
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Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, 2021 In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti +3 more
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A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems
Future Internet, 2022 Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they
Sonya Leech +2 more
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A Non-Parametric Generative Model for Human Trajectories [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Modeling human mobility and synthesizing realistic trajectories play a fundamental role in urban planning and privacy-preserving location data analysis. Due to its high dimensionality and also the diversity of its applications, existing trajectory generative models do not preserve the geometric (and more importantly) semantic features of human ...
Kun Ouyang +3 more
openaire +1 more source
Advances in Electrical and Electronic Engineering, 2021 Due to the rising usage of various broadcasting systems and web-casting applications, a measurement of audio quality has become an essential task. This paper presents a benchmark of the parametric models for non-intrusive estimation of the audio quality ...
Martin Jakubik, Peter Pocta
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Non-parametric Models with Instrumental Variables [PDF]
, 2011 This chapter gives a survey of econometric models characterized by a relation between observable and unobservable random elements where these unobservable terms are assumed to be independent of another set of observable variables called instrumental variables.
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Non-parametric and light-field deformable models [PDF]
Computer Vision and Image Understanding, 2006 Statistical shape-and-texture appearance models use image morphing to define a rich, compact representation of object appearance. They are useful in a variety of applications including object recognition, tracking and segmentation. These techniques, however, have been limited to objects with Lambertian surface reflectance, simple geometry and topology.
C. Mario Christoudias +2 more
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Bayesian non-parametrics and the probabilistic approach to modelling [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2013 Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model.
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Correlated Non-Parametric Latent Feature Models [PDF]
CoRR, 2012 Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)
Finale Doshi-Velez, Zoubin Ghahramani
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A Bayesian natural cubic B-spline varying coefficient method for non-ignorable dropout
BMC Medical Research Methodology, 2020 Background Dropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account
Camille M. Moore +3 more
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