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Transfer Learning with Kernel Methods [PDF]
Nature Communications, 2023 Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop ...
Adityanarayanan Radhakrishnan +3 more
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Integrating Distance Correlation and Adaptive Weighting with RBF Kernel Transformations: A Novel Feature Selection Framework with Application to ECG Arrhythmia Detection [PDF]
BioengineeringAccurate feature selection is critical for machine learning in medical diagnosis, yet conventional methods often fail to capture complex non-linear relationships in biomedical data.
Monica Fira, Lucian Fira
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Random Forests and Kernel Methods [PDF]
IEEE Transactions on Information Theory, 2016 Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods.
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Kernel-Based Independence Tests for Causal Structure Learning on Functional Data
Entropy, 2023 Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering.
Felix Laumann +4 more
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Kernel Geometric Mean Metric Learning
Applied Sciences, 2023 Geometric mean metric learning (GMML) algorithm is a novel metric learning approach proposed recently. It has many advantages such as unconstrained convex objective function, closed form solution, faster computational speed, and interpretability over ...
Zixin Feng +4 more
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, 2023 This chapter introduces a powerful class of machine learning approaches called kernel methods, which present an alternative to arguably more widely known neural network approaches. Kernel methods can learn even highly nonlinear problems by making an implicit transformation from a low-dimensional input space into a higher-dimensional feature space. This
Pinheiro Jr, Max, Dral, Pavlo
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The kernel polynomial method [PDF]
Reviews of Modern Physics, 2006 Efficient and stable algorithms for the calculation of spectral quantities and correlation functions are some of the key tools in computational condensed matter physics. In this article we review basic properties and recent developments of Chebyshev expansion based algorithms and the Kernel Polynomial Method.
Weiße, Alexander +3 more
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Applied Sciences, 2020 COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment.
Lydia González-Serrano +4 more
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Data-Driven Supervised Learning for Life Science Data
Frontiers in Applied Mathematics and Statistics, 2020 Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have
Maximilian Münch +7 more
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Efficient Kernel Cook's Distance for Remote Sensing Anomalous Change Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about the
Jose Antonio Padron-Hidalgo +4 more
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