Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
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Summary
The output of a lassi(cid:12)er should be a alibrated posterior probability to enable post-pro essing and a method to train a kernel lassi with a logit link and a regularized maximum likelihood is proposed.
- Type
- article
- Published
- 1999-01-01
- Cited by
- 7,144
- References
- 21
- OpenAlex
- https://openalex.org/W1618905105
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:56563878
Keywords
Support vector machine, Probabilistic logic, Computer science, Artificial intelligence, Machine learning
References
- A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split
- Linear and Nonlinear Separation of Patterns by Linear Programming
- A Continuous Speech Recognition System Embedding MLP into HMM
- Learning internal representations by error propagation
- Bayesian Interpolation
- Fast Training of Support Vector Machines using Sequential Minimal Optimization
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- Assessing evidentiary value in fire debris analysis by chemometric and likelihood ratio approaches.
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