نتایج جستجو برای: multi step

تعداد نتایج: 711656  

2017

Paying off the mortgage, once a widespread rite of passage for homeowners approaching retirement, has become less common in recent years. Concerns are mounting that the increased prevalence of housing debt among older homeowners could compromise financial security in retirement by expanding housing affordability problems, crimping essential nonhousing spending, increasing vulnerability to home ...

2013
Mikhail Ye Zhuravlev Renat F. Sabirianov Sitaram Jaswal Evgeny Y. Tsymbal E. Y. Tsymbal

2008
Riei Ishizeki Martin Kruczenski Marcus Spradlin Anastasia Volovich

We apply the dressing method to a string solution given by a static string wrapped around the equator of a three-sphere and find that the result is the single spike solution recently discussed in the literature. Further application of the method allows the construction of solutions with multiple spikes. In particular we construct the solution describing the scattering of two single spikes and c...

2013

Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propo...

2013
Pierrick Milhorat Stephan Schlögl Gérard Chollet Jérôme Boudy

While natural language as an interaction modality is increasingly being accepted by users, remaining technological challenges still hinder its widespread employment. Tools that better support the design, development and improvement of these types of applications are required. This demo presents a prototyping framework for Spoken Dialog System (SDS) design which combines existing language techno...

2014
Souhaib Ben Taieb Rob J. Hyndman

Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propo...

Journal: :Journal of Computational and Applied Mathematics 2006

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