Relevant bibliographies by topics / Kernel-based model

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Kernel-based model'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Kernel-based model"

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Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic mod
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Zong, Xinlu, Chunzhi Wang, and Hui Xu. "Density-based Adaptive Wavelet Kernel SVM Model for P2P Traffic Classification." International Journal of Future Generation Communication and Networking 6, no. 6 (2013): 25–36. http://dx.doi.org/10.14257/ijfgcn.2013.6.6.04.

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Shim, Jooyong, and Changha Hwang. "Kernel-based orthogonal quantile regression model." Model Assisted Statistics and Applications 12, no. 3 (2017): 217–26. http://dx.doi.org/10.3233/mas-170396.

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Su, Zhi-gang, Pei-hong Wang, and Zhao-long Song. "Kernel based nonlinear fuzzy regression model." Engineering Applications of Artificial Intelligence 26, no. 2 (2013): 724–38. http://dx.doi.org/10.1016/j.engappai.2012.05.009.

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Fan, Yanqin, and Qi Li. "CONSISTENT MODEL SPECIFICATION TESTS." Econometric Theory 16, no. 6 (2000): 1016–41. http://dx.doi.org/10.1017/s0266466600166083.

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We point out the close relationship between the integrated conditional moment tests in Bierens (1982, Journal of Econometrics 20, 105–134) and Bierens and Ploberger (1997, Econometrica 65, 1129–1152) with the complex-valued exponential weight function and the kernel-based tests in Härdle and Mammen (1993, Annals of Statistics 21, 1926–1947), Li and Wang (1998, Journal of Econometrics 87, 145–165), and Zheng (1996, Journal of Econometrics 75, 263–289). It is well established that the integrated conditional moment tests of Bierens (1982) and Bierens and Ploberger (1997) are more powerful than ke
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Marcella, Peter, Minoi Jacey-Lynn, and Ab Rahman Suriani. "Neutral expression synthesis using kernel active shape model." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2022): 150–57. https://doi.org/10.11591/ijeecs.v20.i1.pp150-157.

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This paper presents a modified kernel-based Active Shape Model for neutralizing and synthesizing facial expressions. In recent decades, facial identity and emotional studies have gained interest from researchers, especially in the works of integrating human emotions and machine learning to improve the current lifestyle. It is known that facial expressions are often associated with face recognition systems with poor recognition rate. In this research, a method of a modified kernel-based active shape model based on statistical-based approach is introduced to synthesize neutral (neutralize) expre
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Wang, Zhijie, Mohamed Ben Salah, Hong Zhang, and Nilanjan Ray. "Shape based appearance model for kernel tracking." Image and Vision Computing 30, no. 4-5 (2012): 332–44. http://dx.doi.org/10.1016/j.imavis.2012.03.003.

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Ma, Xin, and Zhi-bin Liu. "The kernel-based nonlinear multivariate grey model." Applied Mathematical Modelling 56 (April 2018): 217–38. http://dx.doi.org/10.1016/j.apm.2017.12.010.

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Lingyu, Liang, Wenqi Huang, Zhaojie Dong, et al. "Short-term power load forecasting based on combined kernel Gaussian process hybrid model." E3S Web of Conferences 256 (2021): 01009. http://dx.doi.org/10.1051/e3sconf/202125601009.

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As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country. According to the national basic policy of energy conservation and emission reduction, it is urgent to realize the intelligent distribution and management of electricity by prediction. Due to the complex nature of electricity load sequences, the traditional model predicts poor results. As a kernel-based machine learning model, Gaussian Process Mixing (GPM) has high predictive accuracy, can multi-modal prediction and output confidence intervals.
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Qian, Yuqing, Tingting Shang, Fei Guo, et al. "Identification of DNA-binding protein based multiple kernel model." Mathematical Biosciences and Engineering 20, no. 7 (2023): 13149–70. http://dx.doi.org/10.3934/mbe.2023586.

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<abstract> <p>DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linea
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Dissertations / Theses on the topic "Kernel-based model"

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Bose, Aishwarya. "Effective web service discovery using a combination of a semantic model and a data mining technique." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26425/1/Aishwarya_Bose_Thesis.pdf.

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With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s
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Bose, Aishwarya. "Effective web service discovery using a combination of a semantic model and a data mining technique." Queensland University of Technology, 2008. http://eprints.qut.edu.au/26425/.

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With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s
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Zhang, Lin. "Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95040.

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We are living in an era in which many mysteries related to science, technologies and design can be answered by "learning" the huge amount of data accumulated over the past few decades. In the processes of those endeavors, highly-correlated high-dimensional data are frequently observed in many areas including predicting shelf life, controlling manufacturing processes, and identifying important pathways related with diseases. We define a "set" as a group of highly-correlated high-dimensional (HCHD) variables that possess a certain practical meaning or control a certain process, and define an "el
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Ozier-Lafontaine, Anthony. "Kernel-based testing and their application to single-cell data." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0025.

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Les technologies de sequençage en cellule unique mesurent des informations à l’échelle de chaque cellule d’une population. Les données issues de ces technologies présentent de nombreux défis : beaucoup d’observations en grande dimension et souvent parcimonieuses. De nombreuses expériences de biologie consistent à comparer des conditions.L’objet de la thèse est de développer un ensemble d’outils qui compare des échantillons de données issues des technologies de séquençage en cellule unique afin de détecter et décrire les différences qui existent. Pour cela, nous proposons d’appliquer les tests
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Garg, Aditie. "Designing Reactive Power Control Rules for Smart Inverters using Machine Learning." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83558.

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Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid. Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid service
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TREVISO, FELIPE. "Modeling for the Computer-Aided Design of Long Interconnects." Doctoral thesis, Politecnico di Torino, 2022. https://hdl.handle.net/11583/2973429.

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Kim, Byung-Jun. "Semiparametric and Nonparametric Methods for Complex Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99155.

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A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect
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Polajnar, Tamara. "Semantic models as metrics for kernel-based interaction identification." Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/2260/.

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Automatic detection of protein-protein interactions (PPIs) in biomedical publications is vital for efficient biological research. It also presents a host of new challenges for pattern recognition methodologies, some of which will be addressed by the research in this thesis. Proteins are the principal method of communication within a cell; hence, this area of research is strongly motivated by the needs of biologists investigating sub-cellular functions of organisms, diseases, and treatments. These researchers rely on the collaborative efforts of the entire field and communicate through experime
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Lyubchyk, Leonid, Oleksy Galuza, and Galina Grinberg. "Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering." Thesis, ННК "IПСА" НТУУ "КПI iм. Iгоря Сiкорського", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36819.

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The proposed preference learning on clusters method allows to fully realizing the advantages of the kernel-based approach. While the dimension of the model is determined by a pre-selected number of clusters and its complexity do not grow with increasing number of observations. Thus real-time preference function identification algorithm based on training data stream includes successive estimates of cluster parameter as well as average cluster ranks updating and recurrent kernel-based nonparametric estimation of preference model.
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Vlachos, Dimitrios. "Novel algorithms in wireless CDMA systems for estimation and kernel based equalization." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7658.

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A powerful technique is presented for joint blind channel estimation and carrier offset method for code- division multiple access (CDMA) communication systems. The new technique combines singular value decomposition (SVD) analysis with carrier offset parameter. Current blind methods sustain a high computational complexity as they require the computation of a large SVD twice, and they are sensitive to accurate knowledge of the noise subspace rank. The proposed method overcomes both problems by computing the SVD only once. Extensive simulations using MatLab demonstrate the robustness of the prop
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Book chapters on the topic "Kernel-based model"

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Chen, Bo, Hongwei Liu, and Zheng Bao. "General Kernel Optimization Model Based on Kernel Fisher Criterion." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_24.

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Niu, Lu, and Shaobo Li. "Kernel Fence GAN: Unsupervised Anomaly Detection Model Based on Kernel Function." In Intelligence Computation and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4393-3_34.

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Zhang, Yuehua, Peng Zhang, and Yong Shi. "Kernel Based Regularized Multiple Criteria Linear Programming Model." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01973-9_70.

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Travieso, Carlos M., Jesús B. Alonso, Jaime R. Ticay-Rivas, and Marcos del Pozo-Baños. "Apnea Detection Based on Hidden Markov Model Kernel." In Advances in Nonlinear Speech Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25020-0_10.

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Fleischanderl, Gerhard, Thomas Havelka, Herwig Schreiner, Markus Stumptner, and Franz Wotawa. "DiKe - A Model-Based Diagnosis Kernel and Its Application." In KI 2001: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45422-5_31.

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Taylan, Pakize. "Kernel Based C-Bridge Estimator for Partially Nonlinear Model." In Operations Research. CRC Press, 2022. http://dx.doi.org/10.1201/9781003324508-2.

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Hernández-Torruco, José, Juana Canul-Reich, Juan Frausto-Solis, and Juan José Méndez-Castillo. "A Kernel-Based Predictive Model for Guillain-Barré Syndrome." In Advances in Artificial Intelligence and Its Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27101-9_20.

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Rong, Hai-Jun, and Zhao-Xu Yang. "Robust Kernel-Based Model Reference Adaptive Control for Unstable Aircraft." In Sequential Intelligent Dynamic System Modeling and Control. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1541-1_15.

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Zhou, Yifei, and Conor Hayes. "Graph-Based Diffusion Method for Top-N Recommendation." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_23.

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AbstractData that may be used for personalised recommendation purposes can intuitively be modelled as a graph. Users can be linked to item data; item data may be linked to item data. With such a model, the task of recommending new items to users or making new connections between items can be undertaken by algorithms designed to establish the relatedness between vertices in a graph. One such class of algorithm is based on the random walk, whereby a sequence of connected vertices are visited based on an underlying probability distribution and a determination of vertex relatedness established. A
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Kokologiannakis, Michalis, and Viktor Vafeiadis. "GenMC: A Model Checker for Weak Memory Models." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_20.

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AbstractGenMC is an LLVM-based state-of-the-art stateless model checker for concurrent C/C++ programs. Its modular infrastructure allows it to support complex memory models, such as RC11 and IMM, and makes it easy to extend to support further axiomatic memory models.In this paper, we discuss the overall architecture of the tool and how it can be extended to support additional memory models, programming languages, and/or synchronization primitives. To demonstrate the point, we have extended the tool with support for the Linux kernel memory model (LKMM), synchronization barriers, POSIX I/O syste
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Conference papers on the topic "Kernel-based model"

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Liu, Dan, Chenhua Xu, Wenjie Zhang, Jianbin Xiong, and Xi Liu. "Status evaluation model based on improved ELM with mixed-kernel function." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662660.

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Santra, Soumen, Dipankar Majumdar, Surajit Mandal, and Arpan Deyasi. "Kernel Based Fuzzy Simulation Model for Early Detection of Cancer to Avoid Metastasis." In 2024 IEEE International Conference of Electron Devices Society Kolkata Chapter (EDKCON). IEEE, 2024. https://doi.org/10.1109/edkcon62339.2024.10870775.

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Wu, Xiaohui, Yanyan Liao, Xin Luo, Juntong Chen, Yifei He, and Shijunjie Lu. "A lightweight fashion style recognition model based on large kernel separable attention mechanism." In Third Asia Conference on Computer Vision, Image Processing and Pattern Recognition (CVIPPR 2025), edited by Lei Chen. SPIE, 2025. https://doi.org/10.1117/12.3076332.

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Ye, Ting, Chengjun Huang, Canxin Guo, Huiling Dou, and Xiaodan Li. "Service life prediction method of cable insulation faults based on multiple kernel learning model." In Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), edited by Yuanhao Wang and Cristian Paul Chioncel. SPIE, 2024. https://doi.org/10.1117/12.3051093.

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Liu, Shuang, Jingjing Jian, and Jianxun Tang. "A novel sonar image target detection model based on fast large kernel attention network." In 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), edited by Kelin Du and Azlan bin Mohd Zain. SPIE, 2024. http://dx.doi.org/10.1117/12.3038747.

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Aggarwal, Hemant Kumar, Antony Jerald, Phaneendra K. Yalavarthy, Rajesh Langoju, and Bipul Das. "Display Field-of-View Agnostic Robust Ct Kernel Synthesis Using Model-Based Deep Learning." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980685.

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Wang, Changyue, Huifen Chen, Qiyuan Yang, Haoyu Hua, and Lili Hu. "Short-Term Traffic Flow Uncertainty Prediction Based on Deep Kernel Adaptive Interval Grey Model." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019891.

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Yu, Leiming, Xun Gong, Yifan Sun, Qianqian Fang, Norm Rubin, and David Kaeli. "Moka: Model-based concurrent kernel analysis." In 2017 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 2017. http://dx.doi.org/10.1109/iiswc.2017.8167777.

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Zhu, Qi, Yong Xu, JinRong Cui, et al. "A method for constructing simplified kernel model based on kernel-MSE." In 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA 2009). IEEE, 2009. http://dx.doi.org/10.1109/paciia.2009.5406447.

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Chen, Shan, Lingling Zhou, Rendong Ying, and Yi Ge. "Safe device driver model based on kernel-mode JVM." In the 3rd international workshop. ACM Press, 2007. http://dx.doi.org/10.1145/1408654.1408657.

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Reports on the topic "Kernel-based model"

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Helmut, Harbrecht, John Davis Jakeman, and Peter Zaspel. Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1608084.

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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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Sparks, Paul, Jesse Sherburn, William Heard, and Brett Williams. Penetration modeling of ultra‐high performance concrete using multiscale meshfree methods. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41963.

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Terminal ballistics of concrete is of extreme importance to the military and civil communities. Over the past few decades, ultra‐high performance concrete (UHPC) has been developed for various applications in the design of protective structures because UHPC has an enhanced ballistic resistance over conventional strength concrete. Developing predictive numerical models of UHPC subjected to penetration is critical in understanding the material's enhanced performance. This study employs the advanced fundamental concrete (AFC) model, and it runs inside the reproducing kernel particle method (RKPM)
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Manninen, Terhikki, and Pauline Stenberg. Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361379.

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Recently a simple analytic canopy bidirectional reflectance factor (BRF) model based on the spectral invariants theory was presented. The model takes into account that the recollision probability in the forest canopy is different for the first scattering than the later ones. Here this model is extended to include the forest floor contribution to the total forest BRF. The effect of the understory vegetation on the total forest BRF as well as on the simple ratio (SR) and the normalized difference (NDVI) vegetation indices is demonstrated for typical cases of boreal forest. The relative contribut
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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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