Efficient cross-validation for kernelized least-squares regression with sparse basis expansions

DOI10.1007/s10994-012-5287-6zbMath1243.68246OpenAlexW2002643138MaRDI QIDQ439007

Hanna Suominen, Tapio Pahikkala, Jorma Boberg

Publication date: 31 July 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-012-5287-6

zbMATH Keywords

cross-validationkernel methodshold-outleast-squares support vector machineregularized least-squaressparse basis expansions

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

HYPER-PARAMETER SELECTION FOR SPARSE LS-SVM VIA MINIMIZATION OF ITS LOCALIZED GENERALIZATION ERROR

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
• Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression
• Matrix representations, linear transformations, and kernels for disambiguation in natural language
• Inference for the generalization error
• An efficient algorithm for learning to rank from preference graphs
• Optimized fixed-size kernel models for large data sets
• Additive regularization trade-off: fusion of training and validation levels in kernel methods
• Efficient leave-\(m\)-out cross-validation of support vector regression by generalizing decremental algorithm
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Matrix Analysis
• A Statistical View of Some Chemometrics Regression Tools
• Ten More Years of Error Rate Research
• Kernel matching pursuit