Scikit

Scikit-learn: machine learning in python. Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from url{http://scikit-learn.sourceforge.net}.


References in zbMATH (referenced in 596 articles , 1 standard article )

Showing results 1 to 20 of 596.
Sorted by year (citations)

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  1. Ah-Pine, Julien: Learning doubly stochastic and nearly idempotent affinity matrix for graph-based clustering (2022)
  2. Alvaro J. Garcia-Tejedor, Alberto Nogales: GEMA: An open-source Python library for self-organizing-maps (2022) arXiv
  3. Archibald, Richard; Tran, Hoang: A dictionary learning algorithm for compression and reconstruction of streaming data in preset order (2022)
  4. Belli, Edoardo: Smoothly adaptively centered ridge estimator (2022)
  5. Boudabsa, Lotfi; Filipović, Damir: Machine learning with kernels for portfolio valuation and risk management (2022)
  6. Brunet-Saumard, Camille; Genetay, Edouard; Saumard, Adrien: K-bMOM: A robust Lloyd-type clustering algorithm based on bootstrap median-of-means (2022)
  7. Chou, Ping; Chuang, Howard Hao-Chun; Chou, Yen-Chun; Liang, Ting-Peng: Predictive analytics for customer repurchase: interdisciplinary integration of buy till you die modeling and machine learning (2022)
  8. Chung, Yu-Min; Lawson, Austin: Persistence curves: a canonical framework for summarizing persistence diagrams (2022)
  9. Coma-Puig, Bernat; Carmona, Josep: Non-technical losses detection in energy consumption focusing on energy recovery and explainability (2022)
  10. Gahm, Christian; Uzunoglu, Aykut; Wahl, Stefan; Ganschinietz, Chantal; Tuma, Axel: Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning (2022)
  11. Gao, Can; Wang, Zhicheng; Zhou, Jie: Three-way approximate reduct based on information-theoretic measure (2022)
  12. Gillard, Jonathan; O’Riordan, Emily; Zhigljavsky, Anatoly: Simplicial and minimal-variance distances in multivariate data analysis (2022)
  13. Golovkine, Steven; Klutchnikoff, Nicolas; Patilea, Valentin: Clustering multivariate functional data using unsupervised binary trees (2022)
  14. Herath, Sumudu: Multiscale modelling and material design of woven textiles using Gaussian processes (2022)
  15. Hoang, Chi; Chowdhary, Kenny; Lee, Kookjin; Ray, Jaideep: Projection-based model reduction of dynamical systems using space-time subspace and machine learning (2022)
  16. Houman Mirzaalian Dastjerdi, Reza Gholami Mahmoodabadi, Matthias Bär, Vahid Sandoghdar, Harald Köstler: PiSCAT: A Python Package for Interferometric Scattering Microscopy (2022) not zbMATH
  17. Huang, Shan; Zhu, Renchuan; Chang, Hongyu; Wang, Hui; Yu, Yun: Machine learning to approximate free-surface Green’s function and its application in wave-body interactions (2022)
  18. Iliadis, Dimitrios; De Baets, Bernard; Waegeman, Willem: Multi-target prediction for dummies using two-branch neural networks (2022)
  19. Jäger, Georg; Reisinger, Daniel: Can we replicate real human behaviour using artificial neural networks? (2022)
  20. Johannes N. Hansen: nd - A Framework for the Analysis of n-dimensional Earth Observation Data (2022) not zbMATH

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