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Journal articles on the topic 'Non-parametric learning'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Liu, Bing, Shi-Xiong Xia, and Yong Zhou. "Unsupervised non-parametric kernel learning algorithm." Knowledge-Based Systems 44 (May 2013): 1–9. http://dx.doi.org/10.1016/j.knosys.2012.12.008.

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Esser, Pascal, Maximilian Fleissner, and Debarghya Ghoshdastidar. "Non-parametric Representation Learning with Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 11910–18. http://dx.doi.org/10.1609/aaai.v38i11.29077.

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Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been mostly developed in the neural network literature, and other models for representation learning are surprisingly unexplored. In this work, we introduce and analyze several kernel-based representation learning approaches: Firstly, we define two kernel Self-Supervised Learning (SSL) models using contrastive loss functions and secondly, a Kernel Autoencoder (AE) model based on the idea of embedding and reconstructing data.

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Cruz, David Luviano, Francesco José García Luna, and Luis Asunción Pérez Domínguez. "Multiagent reinforcement learning using Non-Parametric Approximation." Respuestas 23, no. 2 (2018): 53–61. http://dx.doi.org/10.22463/0122820x.1738.

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This paper presents a hybrid control proposal for multi-agent systems, where the advantages of the reinforcement learning and nonparametric functions are exploited. A modified version of the Q-learning algorithm is used which will provide data training for a Kernel, this approach will provide a sub optimal set of actions to be used by the agents. The proposed algorithm is experimentally tested in a path generation task in an unknown environment for mobile robots.

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Khadse, Vijay M., Parikshit Narendra Mahalle, and Gitanjali R. Shinde. "Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests." International Journal of Ambient Computing and Intelligence 11, no. 3 (2020): 80–105. http://dx.doi.org/10.4018/ijaci.2020070105.

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The emerging area of the internet of things (IoT) generates a large amount of data from IoT applications such as health care, smart cities, etc. This data needs to be analyzed in order to derive useful inferences. Machine learning (ML) plays a significant role in analyzing such data. It becomes difficult to select optimal algorithm from the available set of algorithms/classifiers to obtain best results. The performance of algorithms differs when applied to datasets from different application domains. In learning, it is difficult to understand if the difference in performance is real or due to

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Yoa, Seungdong, Jinyoung Park, and Hyunwoo J. Kim. "Learning Non-Parametric Surrogate Losses With Correlated Gradients." IEEE Access 9 (2021): 141199–209. http://dx.doi.org/10.1109/access.2021.3120092.

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6

Rutkowski, Leszek. "Non-parametric learning algorithms in time-varying environments." Signal Processing 18, no. 2 (1989): 129–37. http://dx.doi.org/10.1016/0165-1684(89)90045-5.

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Liu, Mingming, Bing Liu, Chen Zhang, and Wei Sun. "Embedded non-parametric kernel learning for kernel clustering." Multidimensional Systems and Signal Processing 28, no. 4 (2016): 1697–715. http://dx.doi.org/10.1007/s11045-016-0440-1.

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Chen, Changyou, Junping Zhang, Xuefang He, and Zhi-Hua Zhou. "Non-Parametric Kernel Learning with robust pairwise constraints." International Journal of Machine Learning and Cybernetics 3, no. 2 (2011): 83–96. http://dx.doi.org/10.1007/s13042-011-0048-6.

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Kaur, Navdeep, Gautam Kunapuli, and Sriraam Natarajan. "Non-parametric learning of lifted Restricted Boltzmann Machines." International Journal of Approximate Reasoning 120 (May 2020): 33–47. http://dx.doi.org/10.1016/j.ijar.2020.01.003.

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10

Wang, Mingyang, Zhenshan Bing, Xiangtong Yao, et al. "Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10157–65. http://dx.doi.org/10.1609/aaai.v37i8.26210.

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Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. We extend meta-RL to broad non-parametric task distribut

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Jung, Hyungjoo, and Kwanghoon Sohn. "Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling." Journal of Korea Multimedia Society 19, no. 9 (2016): 1659–68. http://dx.doi.org/10.9717/kmms.2016.19.9.1659.

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Tanwani, Ajay Kumar, and Sylvain Calinon. "Small-variance asymptotics for non-parametric online robot learning." International Journal of Robotics Research 38, no. 1 (2018): 3–22. http://dx.doi.org/10.1177/0278364918816374.

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Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (M

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13

Meharunnisa S P. "Improving Network Traffic Security with Parametric and Non-parametric Anomaly Detection Techniques." Journal of Information Systems Engineering and Management 10, no. 33s (2025): 897–907. https://doi.org/10.52783/jisem.v10i33s.5669.

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Introduction: Anomaly detection in network traffic is a critical component in multiple domains like IoT, Cloud Computing, cybersecurity and other field, focusing on the identification of malicious activities to preserve the integrity of network systems. Objectives: This research investigates the performance of both parametric and non-parametric machine learning algorithms in detecting anomalies within network traffic datasets. Parametric models such as Logistic Regression and Support Vector Machines (SVM) were evaluated alongside non-parametric methods, including Random Forest and K-Nearest Ne

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14

ZHANG, Chao, and Takuya AKASHI. "Two-Side Agreement Learning for Non-Parametric Template Matching." IEICE Transactions on Information and Systems E100.D, no. 1 (2017): 140–49. http://dx.doi.org/10.1587/transinf.2016edp7233.

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15

Ma, Yuchao, and Hassan Ghasemzadeh. "LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4520–27. http://dx.doi.org/10.1609/aaai.v33i01.33014520.

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Activity recognition is central to many motion analysis applications ranging from health assessment to gaming. However, the need for obtaining sufficiently large amounts of labeled data has limited the development of personalized activity recognition models. Semi-supervised learning has traditionally been a promising approach in many application domains to alleviate reliance on large amounts of labeled data by learning the label information from a small set of seed labels. Nonetheless, existing approaches perform poorly in highly dynamic settings, such as wearable systems, because some algorit

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16

Pareek, Parikshit, Chuan Wang, and Hung D. Nguyen. "Non-parametric probabilistic load flow using Gaussian process learning." Physica D: Nonlinear Phenomena 424 (October 2021): 132941. http://dx.doi.org/10.1016/j.physd.2021.132941.

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17

Naeem, Muhammad, and Sohail Asghar. "Structure learning via non-parametric factorized joint likelihood function." Journal of Intelligent & Fuzzy Systems 27, no. 3 (2014): 1589–99. http://dx.doi.org/10.3233/ifs-141125.

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18

Karumanchi, Sisir, Thomas Allen, Tim Bailey, and Steve Scheding. "Non-parametric Learning to Aid Path Planning over Slopes." International Journal of Robotics Research 29, no. 8 (2010): 997–1018. http://dx.doi.org/10.1177/0278364910370241.

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19

Dervilis, Nikolaos, Thomas E. Simpson, David J. Wagg, and Keith Worden. "Nonlinear modal analysis via non-parametric machine learning tools." Strain 55, no. 1 (2018): e12297. http://dx.doi.org/10.1111/str.12297.

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20

Barut, Emre, and Warren B. Powell. "Optimal learning for sequential sampling with non-parametric beliefs." Journal of Global Optimization 58, no. 3 (2013): 517–43. http://dx.doi.org/10.1007/s10898-013-0050-5.

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21

Lu, Zhong-Lin, Yukai Zhao, Jiajuan Liu, and Barbara Dosher. "Non-parametric Hierarchical Bayesian Modeling of the Learning Curve in Perceptual Learning." Journal of Vision 23, no. 9 (2023): 5752. http://dx.doi.org/10.1167/jov.23.9.5752.

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22

Gaviria-Chavarro, Javier, Isabel Cristina Rojas-Padilla, and Yury Vergara-López. "Virtual Learning Object (VLO) for Teaching and Learning Non-Parametric Statistical Methods." Tecné, Episteme y Didaxis: TED, no. 54 (July 1, 2023): 285–302. http://dx.doi.org/10.17227/ted.num54-14155.

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nterpreting, understanding, and applying statistical knowledge, presents, in many cases, some difficulties for students in the training process. For this reason, and thanks to the rise of information and communication technologies; a virtual object was developed for learning the statistical methods of Kruskal Wallis, Mann Whitney U and Wilcoxon, which are included in non-parametric statistics. The objective of this quasi-experimental design study was to apply the virtual object as a teaching-learning strategy for these three statistical methods after its creation and validation in order to sup

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23

Deco, Gustavo, Ralph Neuneier, and Bernd Schümann. "Non-parametric Data Selection for Neural Learning in Non-stationary Time Series." Neural Networks 10, no. 3 (1997): 401–7. http://dx.doi.org/10.1016/s0893-6080(96)00108-6.

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24

Rajathi, C., and P. Rukmani. "Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers." Alexandria Engineering Journal 112 (January 2025): 384–96. http://dx.doi.org/10.1016/j.aej.2024.10.101.

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25

Pal, Dipan K., and Marios Savvides. "Non-Parametric Transformation Networks for Learning General Invariances from Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4667–74. http://dx.doi.org/10.1609/aaai.v33i01.33014667.

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ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data. NPTNs are a natural generalization of ConvNets and can be optimized directly using gradient descent. Unlike almost all previous works in deep architectures, they make no assumption regarding the structure of the invariances present in the data and in that aspect are flexible and powerful. We also model ConvNets and NPTN

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26

Kardan, Ahmad Agha, and Samira Ghareh Gozlou. "A new non-parametric feature learning for supervised link prediction." International Journal of System Control and Information Processing 1, no. 4 (2015): 319. http://dx.doi.org/10.1504/ijscip.2015.075877.

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27

Zoričić, Davor. "Non-parametric testing of the machine learning electricity prices forecasts." International journal of multidisciplinarity in business and science 10, no. 16 (2024): 5–11. https://doi.org/10.56321/ijmbs.10.16.5.

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This research analyzes forecast accuracy in the day-ahead electricity market. Performance of Random Forest and XGBoost machine learning models is compared based on the day-ahead electricity market data for Germany. Data for 2018 and 2021 is analyzed in order to explore differences in forecast accuracy in the low and high market volatility periods. Initial training data for 2017 is used in order to produce forecasts for 2018 up to one month ahead. The training set is then rolled one month forward thus creating a fixed length rolling window of training and forecast set data for the remainder of

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28

Yang, Z., and C. W. Chan. "Learning control for non-parametric uncertainties with new convergence property." IET Control Theory & Applications 4, no. 10 (2010): 2177–83. http://dx.doi.org/10.1049/iet-cta.2009.0458.

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29

Wang, Yi, Bin Li, Yang Wang, Fang Chen, Bang Zhang, and Zhidong Li. "Robust Bayesian non-parametric dictionary learning with heterogeneous Gaussian noise." Computer Vision and Image Understanding 150 (September 2016): 31–43. http://dx.doi.org/10.1016/j.cviu.2016.05.015.

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30

Li, Der-Chang, and Chun-Wu Yeh. "A non-parametric learning algorithm for small manufacturing data sets." Expert Systems with Applications 34, no. 1 (2008): 391–98. http://dx.doi.org/10.1016/j.eswa.2006.09.008.

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31

Fu, R., D. Xiao, A. G. Buchan, X. Lin, Y. Feng, and G. Dong. "A parametric non-linear non-intrusive reduce-order model using deep transfer learning." Computer Methods in Applied Mechanics and Engineering 438 (April 2025): 117807. https://doi.org/10.1016/j.cma.2025.117807.

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32

Park, Yeonseok, Anthony Choi, and Keonwook Kim. "Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System." Sensors 20, no. 3 (2020): 925. http://dx.doi.org/10.3390/s20030925.

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Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones

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33

Souaissi, Zina, Taha B. M. J. Ouarda, and André St-Hilaire. "Non-parametric, semi-parametric, and machine learning models for river temperature frequency analysis at ungauged basins." Ecological Informatics 75 (July 2023): 102107. http://dx.doi.org/10.1016/j.ecoinf.2023.102107.

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34

Herranz-Matey, Ivan, and Luis Ruiz-Garcia. "New Agricultural Tractor Manufacturer’s Suggested Retail Price (MSRP) Model in Europe." Agriculture 14, no. 3 (2024): 342. http://dx.doi.org/10.3390/agriculture14030342.

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Research investigating models for assessing new tractor pricing is notably scarce, despite its fundamental importance in conducting comprehensive cost analyses. This study aims to identify a model that is both user-friendly and robust, evaluating both parametric and Machine Learning-optimized non-parametric models. Among parametric models, the second-order polynomial model demonstrated superior performance in terms of R-squared (R2) of 0.97469 and a Root Mean Square Error (RMSE) of 15,633. Conversely, Machine Learning-optimized Gaussian Processes Regressions exhibited the most favorable overal

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Maddalena, Emilio T., and Colin N. Jones. "Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Regression Approach." IFAC-PapersOnLine 53, no. 2 (2020): 965–70. http://dx.doi.org/10.1016/j.ifacol.2020.12.1265.

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36

Wang, Dongqi, Haoran Wei, Zhirui Zhang, Shujian Huang, Jun Xie, and Jiajun Chen. "Non-parametric Online Learning from Human Feedback for Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11431–39. http://dx.doi.org/10.1609/aaai.v36i10.21395.

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We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system. However, previous methods require online model updating or additional translation memory networks to achieve high-quality performance, making them inflexible and inefficient in practice. In this paper, we propose a novel non-parametric online learning method without changing the model structure. This approach introduce

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37

Tohill, C., L. Ferreira, C. J. Conselice, S. P. Bamford, and F. Ferrari. "Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning." Astrophysical Journal 916, no. 1 (2021): 4. http://dx.doi.org/10.3847/1538-4357/ac033c.

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38

Wirayasa, I. Ketut Adi, Arko Djajadi, H. Andri Santoso, and Eko Indrajit. "Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 15, no. 4 (2021): 403. http://dx.doi.org/10.22146/ijccs.69366.

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Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, a

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39

Singh, Sumeet, Jonathan Lacotte, Anirudha Majumdar, and Marco Pavone. "Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods." International Journal of Robotics Research 37, no. 13-14 (2018): 1713–40. http://dx.doi.org/10.1177/0278364918772017.

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The literature on inverse reinforcement learning (IRL) typically assumes that humans take actions to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive (RS) IRL to explicitly account for a human’s risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk neutral to worst case. We propose effic

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Syed, Zeeshan, Ilan Rubinfeld, Pat Patton, et al. "Using diagnostic codes for risk adjustment: A non-parametric learning approach." Journal of the American College of Surgeons 211, no. 3 (2010): S99—S100. http://dx.doi.org/10.1016/j.jamcollsurg.2010.06.262.

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41

Nesa, Nashreen, Tania Ghosh, and Indrajit Banerjee. "Non-parametric sequence-based learning approach for outlier detection in IoT." Future Generation Computer Systems 82 (May 2018): 412–21. http://dx.doi.org/10.1016/j.future.2017.11.021.

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42

Nurul Amelina Nasharuddin and Nurul Shuhada Zamri. "Non-Parametric Machine Learning for Pollinator Image Classification: A Comparative Study." Journal of Advanced Research in Applied Sciences and Engineering Technology 34, no. 1 (2023): 106–15. http://dx.doi.org/10.37934/araset.34.1.106115.

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Pollinators play a crucial role in maintaining the health of our planet's ecosystems by aiding in plant reproduction. However, identifying and differentiating between different types of pollinators can be a difficult task, especially when they have similar appearances. This difficulty in identification can cause significant problems for conservation efforts, as effective conservation requires knowledge of the specific pollinator species present in an ecosystem. Thus, the aim of this study is to identify the most effective methods, features, and classifiers for developing a reliable pollinator

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43

Muji, Mujiansyah. "Creative Thinking for PJBL Approach Non-Parametric Analysis." JISAE: Journal of Indonesian Student Assessment and Evaluation 10, no. 2 (2024): 59–65. https://doi.org/10.21009/jisae.v10i2.49241.

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Based on the 2018 PISA results, Indonesia ranked creative thinking at 74th place out of 79 countries. This data shows how important it is that creative thinking needs to be fostered, taught and developed in students. There is a very important approach to developing this capability, namely the PjBL approach by utilizing digital libraries. Such as Google Scholar and Crossref, systematic literature reviews, especially on PGMI UIN Antasari Banjarmasin students. The research results show that with PjBL creative thinking, PGMI UIN Antasari Banjarmasin students are not significantly different from co

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44

Chen, Junjin, and Jiatong Song. "Research on Traffic Flow Prediction Methods Based on Deep Learning." Applied and Computational Engineering 111, no. 1 (2024): 72–80. http://dx.doi.org/10.54254/2755-2721/111/2024ch0096.

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In recent years, traffic flow prediction technology has been transformed from statistics based parametric methods and machine learning driven non-parametric methods to big data driven deep learning methods. This paper summarizes and summarizes the existing methods and improvement measures of long and short term traffic flow prediction based on deep learning. The time range of traffic flow forecast based on the model is divided into long-term and short term. The short-term traffic flow forecasting methods are subdivided into time series model, non-parametric forecasting model and probability fo

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45

Hakim, Abdul, Nurhikmah H. Nurhikmah, Nur Halisa, Farida Febriati, Latri Aras, and Lutfi B. Lutfi. "The Effect of Online Learning on Student Learning Outcomes in Indonesian Subjects." Journal of Innovation in Educational and Cultural Research 4, no. 1 (2023): 133–40. http://dx.doi.org/10.46843/jiecr.v4i1.312.

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This study employs a Pre-Experimental Design (Non-design) to examine whether online learning has any effect on learning outcomes in Indonesian subjects. The sample size for this study was 16 students, chosen at random. Data collection methods include observation, testing, and documentation. Observations were made by observing both teacher and student activities. The test consists of a pretest before implementing offline learning and a posttest after implementing online learning, as well as documentation for research purposes. The data was analyzed using descriptive statistics and the non-param

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46

Shi, Chao, and Yu Wang. "Non-parametric machine learning methods for interpolation of spatially varying non-stationary and non-Gaussian geotechnical properties." Geoscience Frontiers 12, no. 1 (2021): 339–50. http://dx.doi.org/10.1016/j.gsf.2020.01.011.

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Yang, Z., and C. W. Chan. "Conditional iterative learning control for non-linear systems with non-parametric uncertainties under alignment condition." IET Control Theory & Applications 3, no. 11 (2009): 1521–27. http://dx.doi.org/10.1049/iet-cta.2008.0532.

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48

Wang, Menglin, Zhun Zhong, and Xiaojin Gong. "Prior-Constrained Association Learning for Fine-Grained Generalized Category Discovery." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21162–70. https://doi.org/10.1609/aaai.v39i20.35414.

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This paper addresses generalized category discovery (GCD), the task of clustering unlabeled data from potentially known or unknown categories with the help of labeled instances from each known category. Compared to traditional semi-supervised learning, GCD is more challenging because unlabeled data could be from novel categories not appearing in labeled data. Current state-of-the-art methods typically learn a parametric classifier assisted by self-distillation. While being effective, these methods do not make use of cross-instance similarity to discover class-specific semantics which are essen

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Huang, Lei, Yuqing Ma, and Xianglong Liu. "A general non-parametric active learning framework for classification on multiple manifolds." Pattern Recognition Letters 130 (February 2020): 250–58. http://dx.doi.org/10.1016/j.patrec.2019.01.013.

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50

Chakraborty, Aditya, and Mohan D. Pant. "Machine Learning Models for Pancreatic Cancer Survival Prediction: A Multi-Model Analysis Across Stages and Treatments Using the Surveillance, Epidemiology, and End Results (SEER) Database." Journal of Clinical Medicine 14, no. 13 (2025): 4686. https://doi.org/10.3390/jcm14134686.

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Background: Pancreatic cancer is among the most lethal malignancies, with poor prognosis and limited survival despite treatment advances. Accurate survival modeling is critical for prognostication and clinical decision-making. This study had three primary aims: (1) to determine the best-fitting survival distribution among patients diagnosed and deceased from pancreatic cancer across stages and treatment types; (2) to construct and compare predictive risk classification models; and (3) to evaluate survival probabilities using parametric, semi-parametric, non-parametric, machine learning, and de

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