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Relevant bibliographies by topics / Single-target regression

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Academic literature on the topic 'Single-target regression'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Single-target regression"

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Virtsionis Gkalinikis, Nikolaos Virtsionis, Christoforos Nalmpantis, and Dimitris Vrakas. "Variational Regression for Multi-Target Energy Disaggregation." Sensors 23, no. 4 (2023): 2051. http://dx.doi.org/10.3390/s23042051.

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Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in production. Due to the high number of parameters and the different models, training and inference can be very costly. A promising solution to this problem is the design of an NILM system in which all the target appliances can be recognized by only one model. This paper suggests a novel multi-appliance powe

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Stepisnik, Tomaz, Aljaz Osojnik, Saso Dzeroski, and Dragi Kocev. "Option predictive clustering trees for multi-target regression." Computer Science and Information Systems 17, no. 2 (2020): 459–86. http://dx.doi.org/10.2298/csis190928006s.

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Decision trees are one of the most widely used predictive modelling methods primarily because they are readily interpretable and fast to learn. These nice properties come at the price of predictive performance. Moreover, the standard induction of decision trees suffers from myopia: a single split is chosen in each internal node which is selected in a greedy manner; hence, the resulting tree may be sub-optimal. To address these issues, option trees have been proposed which can include several alternative splits in a new type of internal nodes called option nodes. Considering all of this, an opt

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Christofoletti, Marina, Tânia R. B. Benedetti, Felipe G. Mendes, and Humberto M. Carvalho. "Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys." International Journal of Environmental Research and Public Health 18, no. 14 (2021): 7477. http://dx.doi.org/10.3390/ijerph18147477.

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Background: Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population. Objective: We developed a multilevel regression and poststratification (MRP) model to estimate leisure-time physical activity across Brazilian state capitals and evaluated whether the MRP outperforms single-level regression estimates based on the Brazilian cross-sectional national survey VIGITEL (2018). Methods

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Breskvar, Martin, Dragi Kocev, and Sašo Džeroski. "Ensembles for multi-target regression with random output selections." Machine Learning 107 (July 11, 2018): 1673–709. https://doi.org/10.1007/s10994-018-5744-y.

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We address the task of multi-target regression, where we generate global models that simultaneously predict multiple continuous variables. We use ensembles of generalized decision trees, called predictive clustering trees (PCTs), in particular bagging and random forests (RF) of PCTs and extremely randomized PCTs (extra PCTs). We add another dimension of randomization to these ensemble methods by learning individual base models that consider random subsets of target variables, while leaving the input space randomizations (in RF PCTs and extra PCTs) intact. Moreover, we propose a new ensemble pr

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Sun, Jiaqi, Ming Wei, Jiarong Wang, et al. "CenterADNet: Infrared Video Target Detection Based on Central Point Regression." Sensors 24, no. 6 (2024): 1778. http://dx.doi.org/10.3390/s24061778.

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Infrared video target detection is a fundamental technology within infrared warning and tracking systems. In long-distance infrared remote sensing images, targets often manifest as circular spots or even single points. Due to the weak and similar characteristics of the target to the background noise, the intelligent detection of these targets is extremely complex. Existing deep learning-based methods are affected by the downsampling of image features by convolutional neural networks, causing the features of small targets to almost disappear. So, we propose a new infrared video weak-target dete

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Lv, Jieyi, and Zhongsheng Wang. "Improved Double Regression Nonlinear Image Super Resolution Model." International Journal of Advanced Network, Monitoring and Controls 8, no. 2 (2023): 46–53. http://dx.doi.org/10.2478/ijanmc-2023-0055.

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Abstract The existing super resolution reconstruction methods are mainly divided into traditional super resolution reconstruction and deep learning super resolution reconstruction. The main problem faced by traditional super resolution reconstruction algorithms, such as image enlargement and space transformation, is how to establish the mapping relationship between the input image and the target image, and express the pixel value of the target image through the mapping relationship. As a prominent problem, the difficulty of super resolution reconstruction lies in the fact that there is no real

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Rodríguez-Pérez, Raquel, and Jürgen Bajorath. "Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions." Journal of Computer-Aided Molecular Design 35, no. 3 (2021): 285–95. http://dx.doi.org/10.1007/s10822-021-00376-8.

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AbstractMachine learning (ML) enables modeling of quantitative structure–activity relationships (QSAR) and compound potency predictions. Recently, multi-target QSAR models have been gaining increasing attention. Simultaneous compound potency predictions for multiple targets can be carried out using ensembles of independently derived target-based QSAR models or in a more integrated and advanced manner using multi-target deep neural networks (MT-DNNs). Herein, single-target and multi-target ML models were systematically compared on a large scale in compound potency value predictions for 270 huma

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Majid, Hanafi, and Syahid Anuar. "The Benchmarking Native Multi-Output, Regressor Chain and TPOT-MTR Models." Journal of Advanced Geospatial Science & Technology 5, no. 1 (2025): 111–33. https://doi.org/10.11113/jagst.v5n1.98.

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Native multi-output expands its capabilities to include multi-target regression using a genetic programming approach. It outperforms state-of-the-art methods on multi-target regression datasets, enhancing prediction accuracy and decision-making in multi-target regression domains. However, it relies heavily on a single target strategy, which may not capture complex interdependencies between multiple targets. Additionally, the systems lack understanding of how linked targets in multi-target regression are handled, making it difficult for practitioners to determine the real connections. The purpo

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Li, Da, Haoxiang Chai, Qin Wei, Yao Zhang, and Yunhan Xiao. "PACR: Pixel Attention in Classification and Regression for Visual Object Tracking." Mathematics 11, no. 6 (2023): 1406. http://dx.doi.org/10.3390/math11061406.

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Anchor-free-based trackers have achieved remarkable performance in single visual object tracking in recent years. Most anchor-free trackers consider the rectangular fields close to the target center as the positive sample used in the training phase, while they always use the maximum of the corresponding map to determine the location of the target in the tracking phase. Thus, this will make the tracker inconsistent between the training and tracking phase. To solve this problem, we propose a pixel-attention module (PAM), which ensures the consistency of the training and tracking phase through a

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Kocev, Dragi, Sašo Džeroski, Matt D. White, Graeme R. Newell, and Peter Griffioen. "Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition." Ecological Modelling 220, no. 8 (2009): 1159–68. http://dx.doi.org/10.1016/j.ecolmodel.2009.01.037.

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Book chapters on the topic "Single-target regression"

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Sousa, Ricardo, and João Gama. "Co-training Semi-supervised Learning for Single-Target Regression in Data Streams Using AMRules." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60438-1_49.

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Lou Qiang and Obradovic Zoran. "Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-606-5-797.

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The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket which is a set of variables that can shield a certain variable from the target. Instead of relying on the conditional independence test or network structure learning, the new method uses Hilbert-Schmidt Independence criterion as a measure of dependence among variables in a kernel-induced space. This allows effective approximation of the Markov Blanket that consists of multiple dependent features rather than being limited to a single feature. In addition, the new method can remove both irrelevant and redundant features at the same time. This method for discovering the Markov Blanket is applicable to both discrete and continuous variables, whereas previous methods cannot be used directly for continuous features and therefore are not applicable to regression problems. Experimental evaluations on synthetic and benchmark classification and regression datasets provide evidence that the new feature selection method can remove useless variables in low and in high dimensional problems more accurately than existing Markov Blanket based alternatives.

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Ma, Qiankun, Enyuan Xing, and Kui Su. "Detailed Study on the Sedimentary Environment of the Jialingjiang Formation in the Puguang Area, Northeast Sichuan." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230402.

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A large number of drilling data in the Puguang area in northeast Sichuan show that there is a set of high-quality lithium-rich potassium brine layers in the fourth to the fifth member of the Lower Triassic Jialingjiang Formation. This study focuses on the sedimentary environment and lithofacies palaeogeography characteristics of the four to five sub-members of the Jialingjiang Formation in the Puguang area, northeast Sichuan, using field survey, core thin section, single drilling sedimentary microfacies, logging, lithofacies palaeogeography and other data, and carefully depicts the distribution characteristics and evolution rules of single drilling sedimentary microfacies, multiple drilling sedimentary facies profile, and sedimentary facies plane, it is revealed that the salt-bearing interval of the fourth to the fifth period of the Jialingjiang Formation in the region has experienced the transgression-regression cycle evolution formed by the alternate development of evaporation platform and restricted platform, and the potential favorable salt-forming target interval and sedimentary environment are proposed.

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Zhu, Yancheng. "E2 Net: Efficient and Effective Dense Pedestrian Detection Network Based on YOLOv8." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240128.

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The goal of dense pedestrian detection is to accurately identify and locate pedestrians in crowded scenes. The majority of available dense pedestrian detection algorithms are built on a two-stage framework. The two-stage framework generally transforms the target detection task into a regression task by selecting candidate regions. However, two-stage-based approaches have issues with high computational complexity and subpar real-time performance since they necessitate several region suggestions and feature extraction operations. By executing prediction and regression operations directly on the feature map and skipping region suggestion and multi-stage processing, YOLOv8, as a single-stage detection approach, may substantially decrease computational complexity and increase real-time performance. However, it still has shortcomings in small-scale pedestrian detection and occlusion processing. To solve this problem, we propose an efficient and effective dense pedestrian detection method based on YOLOv8, called E2 Net. We introduce an efficient convolution operator, Partial Convolution (PConv), to reduce computational redundancy and memory consumption. Also, we apply PConv to the FasterNet architecture to improve feature extraction efficiency while maintaining performance, enabling efficient spatial feature extraction on multiple devices. In addition, we introduce a novel loss optimization scheme to reduce small-scale pedestrian misses and incorporate a weighted bi-directional feature pyramid network (BiFPN) to achieve a flexible multi-scale feature fusion algorithm with content awareness. Through extensive experiments, it has been verified that E2 Net has higher accuracy and efficiency on dense pedestrian detection tasks than existing state-of-the-art algorithms.

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Chandel, Abhishish, Vijay Shankar, and Sumit Jaswal. "Employing HEC-ResSim 3.1 for Reservoir Operation and Decision Making." In Hydrostatics [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101673.

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The land of Himachal Pradesh is full of small and big rivers which are perennial. This benefit pushes Himachal Pradesh to build more and more dams to generate electricity, provide better irrigation supply to downstream areas and provide flood protection. To better utilize the huge potential of water, management of such reservoirs is the key issue. For this purpose, HEC-ResSim 3.1 is practiced on Pong Dam situated in western Himachal Pradesh. HEC-ResSim is one of the simulation models that possess single or multi-reservoir simulators and can simulate water resources systems. In this study, reservoir elevation and reservoir storage volume management is the target objective. The presented study was subsidized by daily observed data from 1998 up to 2014 of pool elevation, inflow, and outflow discharge. In addition, geometry and hydraulic data from dam and reservoir were employed to develop the platform to create a simulation using HEC-ResSim. Using the available reservoir data the simulation was performed for the 4 months of 2012. Then simulation results were compared with the real-time recorded data at the site. To validate the results, coefficient of determination for operations like reservoir elevation and reservoir storage was generated through regression plot and found more than 95% accurate. Also, Root Mean Square Error (RMSE) was calculated for both reservoir elevation and reservoir storage simulation and found under an acceptable range. This paper shows the utility of HEC-ResSim 3.1 for reservoir operational management and also throws light on the further scope. Finally, there is a discussion of how useful is HEC-ResSim as a reservoir management tool and integration of HEC-ResSim 3.1 with other hydrologic monitoring systems.

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Conference papers on the topic "Single-target regression"

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Grewe, Lynne L., Wendy Zhou, Nicholas Ferreira, and Jesus Rodriguez. "Multi-output and single-output regression models for body fitness analysis in FitnessBody." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIV, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2025. https://doi.org/10.1117/12.3053844.

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Syed, Farrukh Hasan, Muhammad Raza Shah, and Muhammad Atif Tahir. "Improved Single Target Accuracy Using Feature Selection for Multitarget Regression." In 2020 International Conference on Emerging Trends in Smart Technologies (ICETST). IEEE, 2020. http://dx.doi.org/10.1109/icetst49965.2020.9080703.

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Sahin, Z. Meltem, Esra Erten, Nebiye Musaoglu, D. Ekin Canbay, and Gulsen Kaya. "Using single-and multi-target regression to estimate biophysical parameters of crops." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404409.

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Santana, Everton, Saulo Mastelini, and Sylvio Jr. "Deep Regressor Stacking for Air Ticket Prices Prediction." In XIII Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2017. http://dx.doi.org/10.5753/sbsi.2017.6022.

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Purchasing air tickets by the lowest price is a challenging task for consumers since the prices might fluctuate over time influenced by several factors. In order to support users’ decision, some price prediction techniques have been developed. Considering that this problem could be solved by multi-target approaches from Machine Learning, this work proposes a novel method looking forward to obtaining an improvement in air ticket prices prediction. The method, called Deep Regressor Stacking (DRS), applies a naive deep learning methodology to reach more accurate predictions. To evaluate the contr

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Li, Peiyan, Honglian Wang, Christian Böhm, and Junming Shao. "Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/189.

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Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. However, the mainstream of existing online classification techniques are focused on the single-label case, while only a few multi-label stream classification algorithms exist, and they are mainly trained on labeled instances. In this paper, we present a novel Online Semi-supervised Multi-Label learning algorithm (OnSeML) based on label compression and local smooth regression, which allows real-time multi-label pre

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Shieh, Meng-Dar, and Hsin-En Fang. "Using Support Vector Regression in the Study of Product Form Images." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-69150.

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In this paper, Support Vector Regression (SVR) training models using three different kernels: polynomial, Radial Basis Function (RBF), and mixed kernels, are constructed to demonstrate the training performance of unarranged data obtained from 32 virtual 3-D computer models. The 32 samples used as input data for training the three SVR models are represented by the coordination value sets of points extracted from 3-D models built by the 3-D software according to the shapes of 32 actual hairdryer products. To train the SVR model, an adjective (streamline) is used to evaluate all the 32 samples by

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Zhang, Jie, Souma Chowdhury, Achille Messac, and Bri-Mathias Hodge. "Assessing Long-Term Wind Conditions by Combining Different Measure-Correlate-Predict Algorithms." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12695.

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This paper significantly advanced the hybrid measure-correlate-predict (MCP) methodology, enabling it to account for the variations of both wind speed and direction. The advanced hybrid MCP method used the recorded data of multiple reference stations to estimate the long-term wind condition at the target wind plant site with greater accuracy than possible with data from a single reference station. The wind data was divided into different sectors according to the wind direction, and the MCP strategy was implemented for each wind sector separately. The applicability of the proposed hybrid strate

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Li, Yuchen, Haoyi Xiong, Linghe Kong, et al. "MPGraf: a Modular and Pre-trained Graphformer for Learning to Rank at Web-scale (Extended Abstract)." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/937.

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Both Transformer and Graph Neural Networks (GNNs) have been used in learning to rank (LTR), however, they adhere to two distinct yet complementary problem formulations, i.e., ranking score regression based on query-webpage pairs and link prediction within query-webpage bipartite graphs, respectively. Though it is possible to pre-train GNNs or Transformers on source datasets and fine-tune them subject to sparsely annotated LTR datasets separately, the source-target distribution shifts across the pairs and bipartite graphs domains make it extremely difficult to integrate these diverse models int

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Yılmaz, Yavuz, Rainer Kurz, Ayşe Özmen, and Gerhard-Wilhelm Weber. "A New Algorithm for Scheduling Condition-Based Maintenance of Gas Turbines." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43545.

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In developed electricity markets, the deregulation boosted competition among companies participating in the electricity market. Therefore, the enhanced reliability and availability of gas turbine systems is an industry obligation. Not only providing the available power with minimum operation and maintenance costs, but also guaranteeing high efficiency are additional requisites and efficiency loss of the power plants leads to a loss of money for the electricity generation companies. Multivariate Adaptive Regression Spline (MARS) is a modern methodology of statistical learning, data mining and e

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Huang, Zhenjia (Jerry), and Yu Zhang. "Semi-Empirical Single Realization and Ensemble Crest Distributions of Long-Crest Nonlinear Waves." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78192.

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In wave basin model test of an offshore structure, waves that represent the given sea states have to be generated, qualified and accepted for the model test. We normally accept waves in wave calibration tests if the significant wave height, spectral peak period and spectrum match the specified target values. However, for model tests where the responses depend highly on the local wave motions (wave elevation and kinematics) such as wave impact on hull, green water impact on deck and air gap tests, additional qualification checks may be required. For instance, we may need to check wave crest pro

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