Relevant bibliographies by topics / Locally weighted information gain

Academic literature on the topic 'Locally weighted information gain'

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Published: 10 March 2023

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Journal articles on the topic "Locally weighted information gain"

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Huang, Dong, Chang-Dong Wang, and Jian-Huang Lai. "Locally Weighted Ensemble Clustering." IEEE Transactions on Cybernetics 48, no. 5 (May 2018): 1460–73. http://dx.doi.org/10.1109/tcyb.2017.2702343.

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Carter, Brad, and Claus Rinner. "Locally weighted linear combination in a vector geographic information system." Journal of Geographical Systems 16, no. 3 (November 30, 2013): 343–61. http://dx.doi.org/10.1007/s10109-013-0194-3.

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Nielsen, Jens Perch, and Bjørn Lunding Sandqvist. "Credibility Weighted Hazard Estimation." ASTIN Bulletin 30, no. 2 (November 2000): 405–17. http://dx.doi.org/10.2143/ast.30.2.504643.

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AbstractCredibility weighting is helpful in many insurance applications where sparse data crave information from other sources of data. In this paper we aim at estimating a hazard curve using the nonparametric kernel method, where a credibility weighting principle is used locally, so that areas of sparse data for one subgroup can be alleviated by available information from other subgroups. The credibility estimator is found through a Hilbert space projection formulation of Buhlmann-Straub's credibility approach.
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Li, Xiaoyan, Lefei Zhang, and Jane You. "Locally Weighted Discriminant Analysis for Hyperspectral Image Classification." Remote Sensing 11, no. 2 (January 9, 2019): 109. http://dx.doi.org/10.3390/rs11020109.

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A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about 20 % for Indian Pines and 17 % for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data.
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Syed Ibrahim and Chandran. "Compact Weighted Class Association Rule Mining Using Information Gain." International Journal of Data Mining & Knowledge Management Process 1, no. 6 (November 30, 2011): 1–13. http://dx.doi.org/10.5121/ijdkp.2011.1601.

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Singer, Gonen, Roee Anuar, and Irad Ben-Gal. "A weighted information-gain measure for ordinal classification trees." Expert Systems with Applications 152 (August 2020): 113375. http://dx.doi.org/10.1016/j.eswa.2020.113375.

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Li, Yafang, Caiyan Jia, Xiangnan Kong, Liu Yang, and Jian Yu. "Locally Weighted Fusion of Structural and Attribute Information in Graph Clustering." IEEE Transactions on Cybernetics 49, no. 1 (January 2019): 247–60. http://dx.doi.org/10.1109/tcyb.2017.2771496.

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Jiang, Zhengkai, Peng Gao, Chaoxu Guo, Qian Zhang, Shiming Xiang, and Chunhong Pan. "Video Object Detection with Locally-Weighted Deformable Neighbors." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8529–36. http://dx.doi.org/10.1609/aaai.v33i01.33018529.

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Deep convolutional neural networks have achieved great success on various image recognition tasks. However, it is nontrivial to transfer the existing networks to video due to the fact that most of them are developed for static image. Frame-byframe processing is suboptimal because temporal information that is vital for video understanding is totally abandoned. Furthermore, frame-by-frame processing is slow and inefficient, which can hinder the practical usage. In this paper, we propose LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks. LWDN can latently align the high-level features between keyframes and keyframes or nonkeyframes. Inspired by (Zhu et al. 2017a) and (Hetang et al. 2017) who propose to aggregate features between keyframes and keyframes, we adopt brain-inspired memory mechanism to propagate and update the memory feature from keyframes to keyframes. We call this process Memory-Guided Propagation. With such a memory mechanism, the discriminative ability of features in keyframes and non-keyframes are both enhanced, which helps to improve the detection accuracy. Extensive experiments on VID dataset demonstrate that our method achieves superior performance in a speed and accuracy trade-off, i.e., 76.3% on the challenging VID dataset while maintaining 20fps in speed on Titan X GPU.
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Chen, Yubao, Xiao Li, and Elsayed Orady. "Integrated diagnosis using information-gain-weighted radial basis function neural networks." Computers & Industrial Engineering 30, no. 2 (April 1996): 243–55. http://dx.doi.org/10.1016/0360-8352(95)00169-7.

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Zhou, Jiandong, Xiang Li, Xin Wang, Yunpeng Chai, and Qingpeng Zhang. "Locally weighted factorization machine with fuzzy partition for elderly readmission prediction." Knowledge-Based Systems 242 (April 2022): 108326. http://dx.doi.org/10.1016/j.knosys.2022.108326.

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Books on the topic "Locally weighted information gain"

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Arce, Moisés, Michael S. Hendricks, and Marc S. Polizzi. The Roots of Engagement. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780197639672.001.0001.

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Abstract Studies of resource conflicts emphasize the structural characteristics of mining projects and the strategies of pro- and anti-mining groups in the context of large-scale mining. In this book, we take a different approach that looks at individuals living near proposed mines. We argue and show that individuals are drawn to their communities in different ways. Some of them participate in local organizations more than others, and this social engagement sets them apart from each other when it comes to their views and later demands about mining. By participating in local organizations, individuals gain critical information about the threats posed by mining as well as resources to address community concerns. Participation in local organizations also emboldens individuals to challenge industry and/or government actors seeking to expand resource extraction. And finally, participation in local organizations imparts a community worldview that allows community members to see themselves as being in the same boat, thus also rejecting proposals that jeopardize existing community livelihoods. Individuals who are less socially engaged, in contrast, are more open to embracing the opportunities about mining coming from industry and/or government actors. They apply greater weight to the importance of resource extraction to the nation and their own pocketbooks. These individuals are more likely to express supportive views about mining. The book examines this variation in individual attitudes in three sites characterized by protracted mining conflicts in Peru, Nicaragua, and South Africa. Fieldwork and original surveys in host communities near proposed mines support these findings.
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Book chapters on the topic "Locally weighted information gain"

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Huang, Dong, Chang-Dong Wang, and Jian-Huang Lai. "LWMC: A Locally Weighted Meta-Clustering Algorithm for Ensemble Clustering." In Neural Information Processing, 167–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_17.

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Xu, Yu-Lan, Sibao Chen, and Bin Luo. "A Weighted Locally Linear KNN Model for Image Recognition." In Communications in Computer and Information Science, 567–78. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7305-2_48.

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Lu, Dejun, Weifeng Zhang, Kaixuan Cuan, and Pengfei Liu. "Reflectance Estimation Based on Locally Weighted Linear Regression Methods." In Communications in Computer and Information Science, 93–101. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1648-7_8.

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Shili, Maroua, Moufida Hajjaj, and Mohamed Lassaad Ammari. "Weighted-Gain Beam Selection for Beamspace mmWave Massive MIMO Systems." In Advanced Information Networking and Applications, 947–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44041-1_82.

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Bai, Weiwei, Junsheng Ren, Chuan Che, Tieshan Li, and C. L. Philip Chen. "Global Optimal Locally Weighted Learning-Based Identification Modeling for Azimuth Stern Drive Tug Manoeuvring." In Communications in Computer and Information Science, 576–83. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5230-9_56.

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Muthukumaravel, A., S. Purushothaman, and R. Rajeswari. "Development of Locally Weighted Projection Regression for Concurrency Control in Computer-Aided Design Database." In Proceedings of International Conference on Internet Computing and Information Communications, 431–39. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1299-7_40.

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Nicha Andrade, Julijana. "The Role of Civil Society in Implementing the SDGs Locally." In Handbook of Research on Novel Practices and Current Successes in Achieving the Sustainable Development Goals, 369–88. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8426-2.ch019.

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This chapter aims to present the complex relations between the local actors, the municipality of Curitiba, and UNESCO via the UNESCO Creative Cities Network to implement the 2030 SDGs on the local level. From the case study, the chapter explores the shortcomings and Eurocentric approach of the UNESCO Creative Cities Network and how local actors use the network requirements to gain local ownership of the goals' implementation. As a result, a mutually benefiting relationship arises. On the one hand, UNESCO Creative Cities Network widens its membership approach. On the other hand, local municipalities, such as Curitiba, change their structures to accommodate the SDG implementation by opening to multi-stakeholder governance. It applies a light-weighted approach of the inter-organizational framework and orchestration and makes use of document analysis, historical case analysis, and semi-structured interviews for data gathering.
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Olufemi Odusola, Aina, and Ayoade Adedokun. "Cultural Adaptation of a Cardiovascular Health Education Program among Hypertensive Primary Care Patients from Rural Nigeria." In Topics in Primary Care Medicine [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94542.

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Culturally adapted behavioral treatments can optimize care. Following standard guidelines we adapted and tested a hypertension education program among primary care patients with hypertension from rural Nigeria. We reviewed literature and collected qualitative information from hypertensive patients to implement the first three of five cultural adaptation stages: Information gathering, Adaptation design, and Preliminary adaptation testing. Information obtained was used to adapt a Cardiovascular Health Education Program (CHEP) from a similar program Culturally Appropriate Hypertension Education (CAHE). CHEP was evaluated among 149 hypertensive patients using pre, − post design, and performance of behavioral goals. Data were analyzed using descriptive statistics and thematic contents. CHEP retained essential features of CAHE and added new culturally relevant information. Local context factors like substitutability of dietary salt and exercisability with cultural activities motivated healthy behavior and enhanced cultural fitness. Culturally permissive unhealthy practices were discouraged; intentional weight gain (big is beautiful), and non-smoking tobacco use (sniffing, licking). Performance of behavioral goals was outstanding; over 60% practiced self-set goals effectively. Standard cultural adaptation of behavioral education program demonstrated potential to limit cardiovascular diseases among hypertensive patients. Guideline-based culturally adapted intervention increased hypertension self-management capabilities among hypertensive primary care patients from rural Nigeria.
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Kos Kavran, Andrijana, and Bruno Trstenjak. "The Impact of Augmented Reality Experiential Marketing on Tourist Experience Satisfaction." In Advances in Marketing, Customer Relationship Management, and E-Services, 432–54. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5077-9.ch021.

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The purpose of this chapter is to investigate the impact of augmented reality experiential marketing (AREM) on tourist experience satisfaction. The chapter adds to the existing body of literature in the area of tourist experience satisfaction and its attributes and the use of augmented reality in the scope of experiential marketing. An experiment using an augmented reality system was conducted, which included a sample of 432 tourists who visited a tourist destination in Croatia. The data were tested using machine learning methods, namely information gain (IG) technique, K-means method, weighted K nearest neighbor (WKNN) method, and linear regression (LR) method. Findings indicate that augmented reality experiential marketing has a positive impact on tourist experience satisfaction.
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Lupia, Arthur. "Costs and Benefits." In Uninformed Why People Seem to Know So Little about Politics and What We Can Do about It. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780190263720.003.0018.

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Education is seldom “free” for anyone who participates in it. It requires people to give time, effort, or money in exchange for the possibility of increased knowledge and competence. Educators devote resources to presentations, syllabi, textbooks, websites, videos, and other presentations. Every one of these products takes time and effort to prepare. Students also face costs. Education can require a student to sacrifice alternative opportunities, such as spending time with loved ones and engaging in recreational activities. Are these costs worth paying? When costs are a concern, educators gain incentives to provide information that produces positive net benefits. Once we contemplate net benefits and to whom they accrue, the concept of paying too much for an educational outcome becomes a significant concern. If prospective learners believe that an educator is going to ask them to learn information that is of no value to them, they are likely to view the situation as one that imposes time and energy costs in exchange for no tangible benefit. When made such offers, prospective learners tend to opt out. So providing too much (or the wrong) information can be costly, and by reducing others’ willingness to participate, it can cause otherwise promising educational endeavors to fail. In situations when providing information is a way to increase a beneficial competence, another related question arises: Who should pay? Answering this question can be difficult. To see how, suppose that we have identified information that is necessary for a particular audience to increase a valuable competence. Suppose, however, that most of the benefits of this increased competence accrue to someone other than the members of that audience. Such situations are not uncommon in politics. In politics, one group’s actions often affect others. For example, suppose that childless voters in a local school board election will cast the decisive votes on a matter that affects the quality of life for other people’s children. Suppose that it is costly for the childless voters to learn what they need to know in order to make the best choice for the children. Suppose, moreover, that our competence criterion places great weight on the children’s well-being.
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Conference papers on the topic "Locally weighted information gain"

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Bai, Weiwei, Junsheng Ren, Yanying Che, and Teng Zhang. "Locally optimal locally weighted learning black-box identification modeling for tug manoeuvring." In 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS). IEEE, 2017. http://dx.doi.org/10.1109/iccss.2017.8091417.

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Sun, Maobin, and Lisheng Zhang. "Locally Weighted DTW Method Based on Sparse Path Matrix." In 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2020. http://dx.doi.org/10.1109/iciscae51034.2020.9236926.

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Xu, Bingbing, Huawei Shen, Qi Cao, Keting Cen, and Xueqi Cheng. "Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/267.

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Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed.
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Duan, Weil, and Xiang–yang Lu. "Weighted Naive Bayesian Classifier Model Based on Information Gain." In 2010 International Conference on Intelligent System Design and Engineering Application (ISDEA). IEEE, 2010. http://dx.doi.org/10.1109/isdea.2010.226.

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Guan, Chun, and Xiaoqin Zeng. "An Improved ID3 Based on Weighted Modified Information Gain." In 2011 Seventh International Conference on Computational Intelligence and Security (CIS). IEEE, 2011. http://dx.doi.org/10.1109/cis.2011.284.

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Chai, Yi, Guixia Kang, Ningbo Zhang, Yanyan Guo, and Jingning Wang. "An Associative Classifier Adopting Fuzzy Weighted Rules Based on Information Gain." In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE, 2015. http://dx.doi.org/10.1109/cit/iucc/dasc/picom.2015.34.

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Jun, Jae-Yun, and Yves Rakotondratsimba. "Hedging option contracts with locally weighted regression, functional data analysis, and Markov chain Monte Carlo techniques." In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065012.

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Xia, Yan, Jian Shu, Na Xu, and Hui Feng. "Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio." In 8th International Conference on Computer Supported Education. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005748000770084.

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Chen Deli, Zhang Cong, Tao Huamin, and Lu Huanzhang. "UCA gain/phase calibration in the presence of multipath based on weighted subspace fitting." In 2008 International Conference on Information and Automation (ICIA). IEEE, 2008. http://dx.doi.org/10.1109/icinfa.2008.4608214.

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Morris, Clinton B., Amir M. Mirzendehdel, and Morad Behandish. "Topology Optimization With Locally Evaluable Complement Space Connectivity." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-67499.

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Abstract Enforcing connectivity of parts or their complement space during automated design is essential for various manufacturing and functional considerations such as removing powder, wiring internal components, and flowing internal coolant. The global nature of connectivity makes it difficult to incorporate into generative design methods that rely on local decision making, e.g., topology optimization (TO) algorithms whose update rules depend on the sensitivity of objective functions or constraints to locally change the design. Connectivity is commonly corrected for in a post-processing step, which may result in suboptimal designs. We propose a recasting of the connectivity constraint as a locally differentiable violation measure, defined as a “virtual” compliance, modeled after physical (e.g., thermal or structural) compliance. Such measures can be used within TO alongside other objective functions and constraints, using a weighted penalty scheme to navigate tradeoffs. By carefully specifying the boundary conditions of the virtual compliance problem, the designer can enforce connectivity between arbitrary regions of the part’s complement space while satisfying a primary objective function in the TO loop. We demonstrate the effectiveness of our approach using both 2D and 3D examples, show its flexibility to consider multiple virtual domains, and confirm the benefits of considering connectivity in the design loop rather than enforcing it through post-processing.
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