共查询到20条相似文献，搜索用时 578 毫秒
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Hashim A. Hashim Lyndon J. Brown Kenneth McIsaac 《Journal of The Franklin Institute》2019,356(7):4144-4173
This paper formulates the pose (attitude and position) estimation problem as nonlinear stochastic filter kinematics evolved directly on the Special Euclidean Group 3 (). This work proposes an alternate way of potential function selection and handles the problem as a stochastic filtering problem. The problem is mapped from to vector form, using the Rodriguez vector and the position vector, and then followed by the definition of the pose problem in the sense of Stratonovich. The proposed filter guarantees that the errors present in position and Rodriguez vector estimates are semi-globally uniformly ultimately bounded (SGUUB) in mean square, and that they converge to small neighborhood of the origin in probability. Simulation results show the robustness and effectiveness of the proposed filter in presence of high levels of noise and bias associated with the velocity vector as well as body-frame measurements. 相似文献
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Support vector machines regression (SVMR) is an important tool in many machine learning applications. In this paper, we focus on the theoretical understanding of SVMR based on the insensitive loss. For fixed ??≥?0 and general data generating distributions, we show that the minimizer of the expected risk for insensitive loss used in SVMR is a set-valued function called conditional median. We then establish a calibration inequality of insensitive loss under a noise condition on the conditional distributions. This inequality also ensures us to present a nontrivial variance-expectation bound for insensitive loss, and which is known to be important in statistical analysis of the regularized learning algorithms. With the help of the calibration inequality and variance-expectation bound, we finally derive an explicit learning rate for SVMR in some space. 相似文献
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《Information processing & management》2022,59(2):102795
This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years’ worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient ) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time. 相似文献
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《Information processing & management》2022,59(2):102788
Passage ranking has attracted considerable attention due to its importance in information retrieval (IR) and question answering (QA). Prior works have shown that pre-trained language models (e.g. BERT) can improve ranking performance. However, these simple BERT-based methods tend to focus on passage terms that exactly match the question, which makes them easily fooled by the overlapping but irrelevant (distracting) passages. To solve this problem, we propose a self-matching attention-pooling mechanism (SMAP) to highlight the Essential Terms in the question-passage pairs. Further, we propose a hybrid passage ranking architecture, called BERT-SMAP, which combines SMAP with BERT to more effectively identify distracting passages and downplay their influence. BERT-SMAP uses the representations obtained through SMAP to enhance BERT’s classification mechanism as an interaction-focused neural ranker, and as the inputs of a matching function. Experimental results on three evaluation datasets show that our model outperforms the previous best BERT-based approaches, and is comparable to the state-of-the-art method that utilizes a much stronger pre-trained language model. 相似文献