MKLpy: a python-based framework for Multiple Kernel Learning
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Abstract (arXiv)
Multiple Kernel Learning is a recent and powerful paradigm to learn the kernel function from data. In this paper, we introduce MKLpy, a python-based framework for Multiple Kernel Learning. The library provides Multiple Kernel Learning algorithms for classification tasks, mechanisms to compute kernel functions for different data types, and evaluation strategies. The library is meant to maximize the usability and to simplify the development of novel solutions.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2022A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Predictioncited by 11
- 2024Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learningcited by 2
- 2023Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernelscited by 1
- 2023Efficient Convex Algorithms for Universal Kernel Learningcited by 0
- 2025Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxationscited by 0
- 2026A Coulomb Particle Model for Learning Kernel Attention in Transformerscited by 0
- 2026A Coulomb Particle Model for Learning Kernel Attention in Transformers26 Jul 2026
- 2025Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations26 Nov 2025
- 2024Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning3 Jun 2024
- 2023Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels8 Oct 2023
- 2023Efficient Convex Algorithms for Universal Kernel Learning15 Apr 2023
- 2022A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction22 Jan 2022