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Algorithmic statistics, prediction and machine learning [PDF]
, 2015 Algorithmic statistics considers the following problem: given a binary string $x$ (e.g., some experimental data), find a "good" explanation of this data. It uses algorithmic information theory to define formally what is a good explanation.
Milovanov, Alexey
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Statistics versus machine learning [PDF]
Nature Methods, 2018 Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will ...
Bzdok, Danilo +2 more
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Statistics of Solar Wind Electron Breakpoint Energies Using Machine Learning Techniques [PDF]
, 2020 Solar wind electron velocity distributions at 1 au consist of a thermal "core" population and two suprathermal populations: "halo" and "strahl". The core and halo are quasi-isotropic, whereas the strahl typically travels radially outwards along the ...
Bakrania, Mayur R. +6 more
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Statistical Machine Learning for Human Behaviour Analysis [PDF]
Entropy, 2020 Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition [...]
Thomas B. Moeslund +4 more
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Classifier selection with permutation tests [PDF]
, 2017 This work presents a content-based recommender system for machine learning classifier algorithms. Given a new data set, a recommendation of what classifier is likely to perform best is made based on classifier performance over similar known data sets ...
Arias Vicente, Marta +2 more
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Machine Learning for Statistical Modeling [PDF]
ACM Transactions on Design Automation of Electronic Systems, 2021 We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of
Urmimala Roy +5 more
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Learning the Structure for Structured Sparsity [PDF]
, 2015 Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings.
Bach, Francis, Shervashidze, Nino
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Debiased Machine Learning U-statistics
, 2022 We propose a method to debias estimators based on U-statistics with Machine Learning (ML) first-steps. Standard plug-in estimators often suffer from regularization and model-selection biases, producing invalid inferences. We show that Debiased Machine Learning (DML) estimators can be constructed within a U-statistics framework to correct these biases ...
Escanciano, Juan Carlos +1 more
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Anomaly detection for machine learning redshifts applied to SDSS galaxies
, 2015 We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates.
Bonnett, Christopher +5 more
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Kernel methods in machine learning
, 2008 We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel.
Hofmann, Thomas +2 more
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