Name
Abstract | ||
|---|---|---|
In this work, we propose an approach to binary classification based on an extension of Bayes Point Machines. Particularly, we take into account the whole set of hypotheses that are consistent with the data (the so-called version space) and the intrinsic noise in class labeling. We follow a Bayesian approach and compute an approximate posterior distribution for the model parameters, which leads to a predictive distribution over unseen data. The most compelling feature of the proposed model is that it is able to learn the noise present in the data with no additional cost. All the computations are carried out by means of the approximate Bayesian inference algorithm Expectation Propagation. Experimental results indicate that the proposed approach outperforms Support Vector Machines over several of the classification problems studied and is competitive with other Bayesian classification algorithms based on Gaussian Processes. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.patrec.2008.02.022 | Pattern Recognition Letters |
Keywords | Field | DocType |
bayesian methods,support vector machines,kernel methods,bayes point machines,bayes machines,model parameter,expectation propagation,intrinsic noise,bayesian classification,bayesian approach,approximate inference,approximate posterior distribution,classification problem,binary classification,unseen data,posterior distribution,gaussian process,kernel method,support vector machine,bayesian method | Bayesian inference,Naive Bayes classifier,Pattern recognition,Binary classification,Bayesian linear regression,Posterior probability,Artificial intelligence,Bayesian hierarchical modeling,Expectation propagation,Mathematics,Bayes' theorem | Journal |
Volume | Issue | ISSN |
29 | 10 | Pattern Recognition Letters |
Citations | PageRank | References |
2 | 0.39 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Hernández-Lobato | 1 | 440 | 26.10 |
| José Miguel Hernández-Lobato | 2 | 613 | 49.06 |