Relevant bibliographies by topics / Bias-Variance Tradeoff

Contents

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

Academic literature on the topic 'Bias-Variance Tradeoff'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Bias-Variance Tradeoff"

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Chen, Pin-Yu, and Sijia Liu. "Bias-Variance Tradeoff of Graph Laplacian Regularizer." IEEE Signal Processing Letters 24, no. 8 (2017): 1118–22. http://dx.doi.org/10.1109/lsp.2017.2712141.

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Briscoe, Erica, and Jacob Feldman. "Conceptual complexity and the bias/variance tradeoff." Cognition 118, no. 1 (2011): 2–16. http://dx.doi.org/10.1016/j.cognition.2010.10.004.

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Annan Li, Shiguang Shan, and Wen Gao. "Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition." IEEE Transactions on Image Processing 21, no. 1 (2012): 305–15. http://dx.doi.org/10.1109/tip.2011.2160957.

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Doroudi, Shayan. "The Bias-Variance Tradeoff: How Data Science Can Inform Educational Debates." AERA Open 6, no. 4 (2020): 233285842097720. http://dx.doi.org/10.1177/2332858420977208.

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In addition to providing a set of techniques to analyze educational data, I claim that data science as a field can provide broader insights to education research. In particular, I show how the bias-variance tradeoff from machine learning can be formally generalized to be applicable to several prominent educational debates, including debates around learning theories (cognitivist vs. situativist and constructivist theories) and pedagogy (direct instruction vs. discovery learning). I then look to see how various data science techniques that have been proposed to navigate the bias-variance tradeof
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Yu, Tian-jun, and Xue-feng Yan. "Robust multi-layer extreme learning machine using bias-variance tradeoff." Journal of Central South University 27, no. 12 (2020): 3744–53. http://dx.doi.org/10.1007/s11771-020-4574-9.

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Kim, Dongjae, Jaeseung Jeong, and Sang Wan Lee. "Prefrontal solution to the bias-variance tradeoff during reinforcement learning." Cell Reports 37, no. 13 (2021): 110185. http://dx.doi.org/10.1016/j.celrep.2021.110185.

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Zhou, Qing. "Asset Pricing Model Uncertainty: A Tradeoff between Bias and Variance." International Review of Finance 17, no. 2 (2016): 289–324. http://dx.doi.org/10.1111/irfi.12112.

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Persson, Mats, and Fredrik Grönberg. "Bias-variance tradeoff in anticorrelated noise reduction for spectral CT." Medical Physics 44, no. 9 (2017): e242-e254. http://dx.doi.org/10.1002/mp.12322.

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Stankovic, L. "Performance Analysis of the Adaptive Algorithm for Bias-to-Variance Tradeoff." IEEE Transactions on Signal Processing 52, no. 5 (2004): 1228–34. http://dx.doi.org/10.1109/tsp.2004.826179.

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Zou, Yao, Changchun Gao, Meng Xia, and Congyuan Pang. "Credit scoring based on a Bagging-cascading boosted decision tree." Intelligent Data Analysis 26, no. 6 (2022): 1557–78. http://dx.doi.org/10.3233/ida-216228.

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Establishing precise credit scoring models to predict the potential default probability is vital for credit risk management. Machine learning models, especially ensemble learning approaches, have shown substantial progress in the performance improvement of credit scoring. The Bagging ensemble approach improves the credit scoring performance by optimizing the prediction variance while boosting ensemble algorithms reduce the prediction error by controlling the prediction bias. In this study, we propose a hybrid ensemble method that combines the advantages of the Bagging ensemble strategy and boo
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Dissertations / Theses on the topic "Bias-Variance Tradeoff"

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Visa, Sofia. "Comparative Study of Methods for Linguistic Modeling of Numerical Data." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1043254774.

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Shandilya, Sharad. "ASSESSMENT AND PREDICTION OF CARDIOVASCULAR STATUS DURING CARDIAC ARREST THROUGH MACHINE LEARNING AND DYNAMICAL TIME-SERIES ANALYSIS." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3198.

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In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viabi
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Murali, Sukumar. "Analysis of an Interferometric Stokes Imaging Polarimeter." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/194148.

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Estimation of Stokes vector components from an interferometric fringe encoded image is a novel way of measuring the State Of Polarization (SOP) distribution across a scene. Imaging polarimeters employing interferometric techniques encode SOP information in a single image in the form of fringes. The lack of moving parts and the use of a single image eliminates the problems of conventional polarimetry - vibration, spurious signal generation due to artifacts, beam wander and the need for registration routines. However, interferometric polarimeters are limited by narrow band pass operation and sho
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Neal, Brayden. "On the bias-variance tradeoff : textbooks need an update." Thèse, 2019. http://hdl.handle.net/1866/23786.

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L’objectif principal de cette thèse est de souligner que le compromis biais-variance n’est pas toujours vrai (p. ex. dans les réseaux neuronaux). Nous plaidons pour que ce manque d’universalité soit reconnu dans les manuels scolaires et enseigné dans les cours d’introduction qui couvrent le compromis. Nous passons d’abord en revue l’historique du compromis entre les biais et les variances, sa prévalence dans les manuels scolaires et certaines des principales affirmations faites au sujet du compromis entre les biais et les variances. Au moyen d’expériences et d’analyses approfondies, n
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Book chapters on the topic "Bias-Variance Tradeoff"

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Wilson, Richard C., and Edwin R. Hancock. "Bias-variance tradeoff for adaptive surface meshes." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054758.

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Keijzer, Maarten, and Vladan Babovic. "Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff – Introductory Investigations." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-46239-2_6.

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Shekhovtsov, Alexander. "Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92659-5_8.

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"8 Model Assessment – Bias-Variance Tradeoff." In Practical AI for Business Leaders, Product Managers, and Entrepreneurs. De Gruyter, 2022. http://dx.doi.org/10.1515/9781501505737-008.

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"Smoothers, The Bias-Variance Tradeoff, and the Smoothed Periodogram." In Basic Data Analysis for Time Series with R. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593233.ch9.

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Conference papers on the topic "Bias-Variance Tradeoff"

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Bouchard, Guillaume. "Bias-variance tradeoff in hybrid generative-discriminative models." In Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.85.

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Duplay, Thibault, Henry Lam, and Xinyu Zhang. "ACHIEVING OPTIMAL BIAS-VARIANCE TRADEOFF IN ONLINE DERIVATIVE ESTIMATION." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632325.

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Mittas, Nikolaos, and Lefteris Angelis. "Managing the Uncertainty of Bias-Variance Tradeoff in Software Predictive Analytics." In 2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2016. http://dx.doi.org/10.1109/seaa.2016.30.

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Koppel, Alec, Amrit S. Bedi, and Ketan Rajawat. "Controlling the Bias-Variance Tradeoff via Coherent Risk for Robust Learning with Kernels." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814879.

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Winkelmann, Max, Constantin Vasconi, and Steffen Muller. "Transfer Importance Sampling - How Testing Automated Vehicles in Multiple Test Setups Helps With the Bias-Variance Tradeoff." In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9922091.

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Lang, Stefan, Wolfgang Brunauer, and Julian Granna. "Proposing a global model to overcome the bias-variance tradeoff in the context of hedonic house price models." In 28th Annual European Real Estate Society Conference. European Real Estate Society, 2022. http://dx.doi.org/10.15396/eres2022_186.

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Suzuki, Taiji, Hiroshi Abe, Tomoya Murata, et al. "Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error." 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.2020/393.

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Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. The concept of model compression is also important for analyzing the generalization error of deep learning, known as the compression-based error bound. However, there is still huge gap between a practically effective compression method and its rigorous background of statistical learning theory. To resolve this issue, we develop a new theoretical framework for model compression and propose a new pruning method called {
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Sharma, Rahul, Aditya V. Nori, and Alex Aiken. "Bias-variance tradeoffs in program analysis." In POPL '14: The 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. ACM, 2014. http://dx.doi.org/10.1145/2535838.2535853.

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