共查询到20条相似文献，搜索用时 25 毫秒
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Given multiple prediction problems such as regression or classification, we are interested in a joint inference framework
that can effectively share information between tasks to improve the prediction accuracy, especially when the number of training
examples per problem is small. In this paper we propose a probabilistic framework which can support a set of latent variable
models for different multi-task learning scenarios. We show that the framework is a generalization of standard learning methods
for single prediction problems and it can effectively model the shared structure among different prediction tasks. Furthermore,
we present efficient algorithms for the empirical Bayes method as well as point estimation. Our experiments on both simulated
datasets and real world classification datasets show the effectiveness of the proposed models in two evaluation settings:
a standard multi-task learning setting and a transfer learning setting. 相似文献
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Ryan S. J. d. Baker Albert T. Corbett Ido Roll Kenneth R. Koedinger 《User Modeling and User-Adapted Interaction》2008,18(3):287-314
Some students, when working in interactive learning environments, attempt to “game the system”, attempting to succeed in the
environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to
answer correctly. In this paper, we present a system that can accurately detect whether a student is gaming the system, within
a Cognitive Tutor mathematics curricula. Our detector also distinguishes between two distinct types of gaming which are associated
with different learning outcomes. We explore this detector’s generalizability, and find that it transfers successfully to
both new students and new tutor lessons.
相似文献
| Kenneth R. KoedingerEmail: |
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《The Journal of Strategic Information Systems》2022,31(4):101744
This paper examines how organizations create data-driven value propositions. Data-driven value propositions define what customer value is created based on data. We study the dynamics underlying this process in a European postal-service organization. We develop a model that shows that the process of creating data-driven value propositions is emergent, consisting of iterative resourcing cycles. We find that creating data-driven value propositions involves the performance of two types of resourcing actions: data reconstructing and data repurposing. The process is shaped by two types of data qualities: apparent qualities, i.e., qualities perceived ex-ante as potentially significant for creating value propositions; and latent qualities, which raise unforeseen consequences en route. We discuss the implications of these findings for the literature on creating data-driven value propositions, for our understanding of data as a strategic resource, and for the literature on resourcing. 相似文献
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Non-linear data-driven symbolic models have been gaining traction in many fields due to their distinctive combination of modeling expressiveness and interpretability. Despite that, they are still rather unexplored for ensemble wind speed forecasting, leaving behind new promising avenues for advancing the development of more accurate models which impact the efficiency of energy production. In this work, we develop a methodology based on the evolutionary algorithm known as grammatical evolution, and apply it to build forecasting models of near-surface wind speed over five locations in northeastern Brazil. Taking advantage of the symbolic nature of the models built, we conducted an extensive series of post-analyses. Overall, our models reduced the forecasting errors by 7%–56% when compared with other techniques, including a real-world operational ensemble model used in Brazil. 相似文献
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The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data-driven process identification. EM is an algorithm for maximum likelihood estimation of parameters and ensures convergence of the likelihood function. In presence of missing variables and in ill conditioned problems, EM algorithm greatly assists the design of more robust identification algorithms. Such situations frequently occur in industrial environments. Missing observations due to sensor malfunctions, multiple process operating conditions and unknown time delay information are some of the examples that can resort to the EM algorithm. In this article, a review on applications of the EM algorithm to address such issues is provided. Future applications of EM algorithm as well as some open problems are also provided. 相似文献
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Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant impact on the final results. This work addresses the issue of fine-tuning hyperparameters of Deep Boltzmann Machines using metaheuristic optimization techniques with different backgrounds, such as swarm intelligence, memory- and evolutionary-based approaches. Experiments conducted in three public datasets for binary image reconstruction showed that metaheuristic techniques can obtain reasonable results. 相似文献
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With the growing adoption of Building Information Modeling (BIM), specialized applications have been developed to perform domain-specific analyses. These applications need tailored information with respect to a BIM model element’s attributes and relationships. In particular, architectural elements need further qualification concerning their geometric and functional ‘subtypes’ to support exact simulations and compliance checks. BIM and its underlying data schema, the Industry Foundation Classes (IFC), provide a rich representation with which to exchange semantic entity and relationship data. However, subtypes for individual elements are not represented by default and often require manual designation, leaving it vulnerable to errors and omissions. Existing research to enrich the semantics of IFC model entities employed domain-specific rule sets that scrutinize their legitimacy and modify them, if and when necessary. However, such an approach is limited in their scalability and comprehensibility. This study explored the use of 3D geometric deep neural networks originating from computer vision research. Specifically, Multi-view CNN(MVCNN) and PointNet were investigated to determine their applicability in extracting unique features of door (IfcDoor) and wall (IfcWall) element subtypes, and in turn be leveraged to automate subtype classifications. Test results indicated MVCNN as having the best prediction performance, while PointNet’s accuracy was hampered by resolution loss due to selective use of point cloud data. The research confirmed deep neural networks as a viable solution to distinguishing BIM element subtypes, the critical factor being their ability to detect subtle differences in local geometries. 相似文献
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This study aims to apply seven data-driven methods (i.e. artificial neural networks [ANNs], classification and regression trees [CARTs], fuzzy habitat suitability models [FHSMs], generalized additive models [GAMs], generalized linear models [GLMs], random forests [RF] and support vector machines [SVMs]) to develop data-driven species distribution models (SDMs) for spawning European grayling (Thymallus thymallus), and to compare the predictive performance and the ecological relevance, quantified by the habitat information retrieved from these SDMs (i.e. variable importance and habitat suitability curves [HSCs]). The results suggest RF to yield the most accurate SDM, followed by SVM, CART, ANN, GAM, FHSM and GLM. However, inconsistencies between different performance measures were observed, indicating that different models may obtain a high score on a particular aspect and perform worse on other aspects. Despite their lower predictive ability, GAM, GLM and FHSM proved to be useful, since HSCs could be obtained and thus these techniques allow testing of ecological relevance and habitat suitability. Water depth and flow velocity appeared to be important variables for spawning grayling. The HSCs clearly indicate higher habitat suitability at a lower water depth, a low to medium flow velocity and a higher percentage of medium-sized gravel, whereas the models disagreed on the habitat suitability for the percentage of small-sized gravel. These findings demonstrate the applicability of data-driven SDMs for both habitat prediction and ecological knowledge extraction that are useful for management of a target species. 相似文献
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机器学习算法包括传统机器学习算法和深度学习算法。传统机器学习算法在中医诊疗领域中的应用研究较多,为探究中医辩证规律提供了参考,也为中医诊疗过程的客观化提供了依据。与此同时,随着其在多个领域不断取得成功,深度学习算法在中医诊疗中的价值越来越多地得到业界的重视。通过对中医诊疗领域中使用到的传统机器学习算法与深度学习算法进行述评,总结了两类算法在中医领域中的研究与应用现状,分析了两类算法的特点以及对中医的应用价值,以期为机器学习算法在中医诊疗领域的进一步研究提供参考。 相似文献
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针对高炉炼铁过程,本文提出一种基于即时学习的高炉铁水质量自适应预测控制方法(JITL–APC).该方法的特点是控制器通过k向量近邻(k–VNN)方法搜索数据库中的输入输出(I/O)数据信息,对非线性系统进行局部建模,并在此基础上计算控制律.而且,该方法中引入了工业异常数据处理机制,利用JITL学习子集中的平均数据项,对异常数据项进行填补或替换,从而消除异常数据对控制系统的影响.此外,本文提出一种JITL模型保留策略(MRS),避免由于数据库中相似数据样本不足导致的局部模型严重失配,并通过实时收集I/O数据更新数据库,使控制器自适应不同的工况条件, MRS还可以有效抑制噪声干扰的影响,从而提高控制系统的稳定性.最后,基于某大型钢铁厂2#高炉的数值仿真实验,充分验证了该方法的有效性. 相似文献
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在针对控制和机器人的机器学习任务中, 高斯过程回归是一种常用方法, 具有无参数学习技术的优点. 然而, 它在面对大量训练数据时存在计算量大的缺点, 因此并不适用于实时更新模型的情况. 为了减少这种计算量, 使模型能够通过实时产生的大量数据不断更新, 本文提出了一种基于概率关联的局部高斯过程回归算法. 与其他局部回归模型相比, 该算法通过对多维局部空间模型边界的平滑处理, 使用紧凑支持的概率分布来划分局部模型中的数据, 得到了更好的预测精度. 另外, 还对更新预测矢量的计算方法进行了改进, 并使用k-d树最近邻搜索减少数据分配和预测的时间. 实验证明, 该算法在保持全局高斯过程回归预测精度的同时, 显著提升了计算效率, 并且预测精度远高于其他局部高斯过程回归模型. 该模型能够快速更新和预测, 满足工程中的在线学习的需求. 相似文献
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Industrial robots (IRs) are widely used to increase productivity and efficiency in manufacturing industries. Therefore, it is critical to reduce the energy consumption of IRs to maximize their use in polishing, assembly, welding, and handling tasks. This study adopted a data-driven modeling approach using a batch-normalized long short-term memory (BN-LSTM) network to construct a robust energy-consumption prediction model for IRs. The adopted method applies batch normalization (BN) to the input-to-hidden transition to allow faster convergence of the model. We compared the prediction accuracy with that of the 1D-ResNet14 model in a UR (UR3e and UR10e) public database. The adopted model achieved a root mean square (RMS) error of 2.82 W compared with the error of 6.52 W achieved by 1D-ResNet14 model prediction, indicating a performance improvement of 56.74%. We also compared the prediction accuracy over the UR3e dataset using machine learning and deep learning models, such as regression trees, linear regression, ensemble trees, support vector regression, multilayer perceptron, and convolutional neural network-gated recurrent unit. Furthermore, the layers of the well-trained UR3e power model were transferred to the UR10e cobot to construct a rapid power model with 80% reduced UR10e datasets. This transfer learning approach showed an RMS error of 3.67 W, outperforming the 1D-ResNet14 model (RMS error: 4.78 W). Finally, the BN-LSTM model was validated using unseen test datasets from the Yaskawa polishing motion task, with an average prediction accuracy of 99%. 相似文献
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This paper presents a novel predictive modeling framework for forecasting the future returns of financial markets. The task is very challenging as the movements of the financial markets are volatile, chaotic, and nonlinear in nature. For accomplishing this arduous task, a three-stage approach is proposed. In the first stage, fractal modeling and recurrence analysis are used, and the efficient market hypothesis is tested to comprehend the temporal behavior in order to investigate autoregressive properties. In the second stage, Granger causality tests are applied in a vector auto regression environment to explore the causal interaction structures among the indexes and identify the explanatory variables for predictive analytics. In the final stage, the maximal overlap discrete wavelet transformation is carried out to decompose the stock indexes into linear and nonlinear subcomponents. Seven machine and deep learning algorithms are then applied on the decomposed components to learn the inherent patterns and predicting future movements. For numerical testing, the daily closing prices of four major Asian emerging stock indexes, exhibiting non-stationary behavior, during the period January 2012 to January 2017 are considered. Statistical analyses are performed to ascertain the comparative performance assessment. The obtained results prove the effectiveness of the proposed framework. 相似文献
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深度学习在智能机器人中的应用研究综述 总被引：1，自引：0，他引：1
机器人发展的趋势是人工智能化,深度学习是智能机器人的前沿技术,也是机器学习领域的新课题。深度学习技术被广泛运用于农业、工业、军事、航空等领域,与机器人的有机结合能设计出具有高工作效率、高实时性、高精确度的智能机器人。为了增强智能机器人在各方面的能力,使其更智能化,介绍了深度学习与机器人有关的研究项目与深度学习在机器人中的各种应用,包括室内和室外的场景识别、机器人的工业服务和家庭服务以及多机器人协作等。最后,对深度学习在智能机器人中应用的未来发展、可能面临的机遇和挑战等进行了讨论。 相似文献
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随着环保要求的不断提高,城市集中供暖小锅炉被逐步关停,并被接入城市主干网,热网不断扩张。与此同时,热量的生产也运用地热、太阳能、工业余热、电热等多种热源,使得集中供热系统变得更加复杂。靠传统手工运算方式、或者理想机理建模方式较难对热网的结构设计及运行进行科学优化,需要通过计算机仿真建模的手段,并结合实际热网运行的数据对热网进行阻力特性辨识,才能真正起到有效的作用。本文研究了基于数据驱动与机理模型融合的集中供热网水力平衡分析模型,并利用来自热网SCADA运行数据通过多种机器学习算法对先验知识模型的参数进行学习优化,最终建立与真实热网相匹配的水力分析模型,此种方法可为热力企业的热网结构优化改造、经济运行提供技术参考。 相似文献
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针对传统的主动学习算法只能处理中小型数据集的问题,提出一种基于MapReduce的大数据主动学习算法。首先,在有类别标签的初始训练集上,用极限学习机（ELM）算法训练一个分类器,并将其输出用软最大化函数变换为一个后验概率分布。然后,将无类别标签的大数据集划分为l个子集,并部署到l个云计算节点上。在每一个节点,用训练出的分类器并行地计算各个子集中样例的信息熵,并选择信息熵大的前q个样例进行类别标注,将标注类别的l×q个样例添加到有类别标签的训练集中。重复以上步骤直到满足预定义的停止条件。在Artificial、Skin、Statlog和Poker 4个数据集上与基于ELM的主动学习算法进行了比较,结果显示,所提算法在4个数据集上均能完成主动样例选择,而基于ELM的主动学习算法只在规模最小的数据集上能完成主动样例选择。实验结果表明,所提算法优于基于极限学习机的主动学习算法。 相似文献
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Alejandro Jara 《Computational statistics & data analysis》2007,51(11):5402-5415
The multivariate probit model is a popular choice for modelling correlated binary responses. It assumes an underlying multivariate normal distribution dichotomized to yield a binary response vector. Other choices for the latent distribution have been suggested, but basically all models assume homogeneity in the correlation structure across the subjects. When interest lies in the association structure, relaxing this homogeneity assumption could be useful. The latent multivariate normal model is replaced by a location and association mixture model defined by a Dirichlet process. Attention is paid to the parameterization of the covariance matrix in order to make the Bayesian computations convenient. The approach is illustrated on a simulated data set and applied to oral health data from the Signal Tandmobiel® study to examine the hypothesis that caries is mainly a spatially local disease. 相似文献
