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Efficient model selection for kernel logistic regression.
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Kernel logistic regression models, like their linear counterparts, can be trained using the efficient iteratively reweighted least-squares (IRWLS) algorithm. This approach suggests an approximate leave-one-out cross-validation estimator based on an existing method for exact leave-one-out cross-validation of least-squares models. Results compiled over seven benchmark datasets are presented for kernel logistic regression with model selection procedures based on both conventional k-fold and approximate leave-one-out cross-validation criteria. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Benchmark Datasets Linear Counterpart Least-squares Model Exact Leave-one-out Cross-validation Kernel Logistic Regression Model Approximate Leave-one-out Cross-validation Estimator Efficient Model Selection Approximate Leave-one-out Cross-validation Criterion Conventional K-fold Kernel Logistic Regression Model Selection Procedure |
| Content Type | Text |
