Metric learning for kernel ridge regression: assessment of molecular similarity | Zendy

Raimón Fabregat | Zendy; Puck van Gerwen | Zendy; Matthieu Haeberle | Zendy; Friedrich Eisenbrand | Zendy; Clémence Corminbœuf | Zendy

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Metric learning for kernel ridge regression: assessment of molecular similarity

Author(s) -

Raimón Fabregat,

Puck van Gerwen,

Matthieu Haeberle,

Friedrich Eisenbrand,

Clémence Corminbœuf

Publication year - 2022

Publication title -

machine learning science and technology

Language(s) - English

Resource type - Journals

ISSN - 2632-2153

DOI - 10.1088/2632-2153/ac8e4f

Subject(s) - artificial intelligence , kernel (algebra) , principal component regression , metric (unit) , feature (linguistics) , feature vector , estimator , mathematics , euclidean distance , similarity (geometry) , pattern recognition (psychology) , kernel regression , benchmark (surveying) , machine learning , kernel method , computer science , transformation (genetics) , support vector machine , statistics , principal component analysis , image (mathematics) , discrete mathematics , geography , linguistics , operations management , philosophy , biochemistry , geodesy , chemistry , economics , gene

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