The NN-Stacking: Feature weighted linear stacking through neural networks
نویسندگان
چکیده
منابع مشابه
Feature-Weighted Linear Stacking
Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models. Recent work has shown that the use of meta-features, additional inputs describing each example in a dataset, can boost the performance of ensemble methods, but the greatest reported gains have come from nonlinear procedures requiring significant tuning a...
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The Deep Stacking Network (DSN) is a special type of deep architecture developed to enable and benefit from parallel learning of its model parameters on large CPU clusters. As a prospective key component of future speech recognizers, the architectural design of the DSN and its parallel training endow the DSN with scalability over a vast amount of training data. In this paper, we present our fir...
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The Deep Stacking Network (DSN) is a special type of deep architecture developed to enable and benefit from parallel learning of its model parameters on large CPU clusters. As a prospective key component of future speech recognizers, the architectural design of the DSN and its parallel training endow the DSN with scalability over a vast amount of training data. In this paper, we present our fir...
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We show that either of the following hypotheses imply that there is an inner model with a proper class of strong cardinals and a proper class of Woodin cardinals. 1) There is a countably closed cardinal κ ≥ א3 such that κ and (κ) fail. 2) There is a cardinal κ such that κ is weakly compact in the generic extension by Col(κ, κ). Of special interest is 1) with κ = א3 since it follows from PFA by ...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2020
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2020.02.073