A scalable learning algorithm for Kernel Probabilistic Classifier

Abstract : In this paper we propose a probabilistic classification algorithm that learns a set of kernel functions that associate a probability distribution over classes to an input vector. This model is obtained by maximizing a measure over the probability distributions through a local optimization process. This measure focuses on the faithfulness of the whole probability distribution induced rather than only considering the probabilities of the classes separately. We show that, thanks to a pre-processing computation, the complexity of the evaluation of this measure with respect to a model is no longer dependent on the size of the training set. This makes the local optimization of the whole set of kernel functions tractable, even for large databases. We experiment our method on five benchmark datasets and the KDD Cup 2012 dataset.
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01171944
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Monday, July 6, 2015 - 3:20:13 PM
Last modification on : Thursday, June 27, 2019 - 4:27:49 PM
Long-term archiving on : Wednesday, April 26, 2017 - 12:29:08 AM

File

serrurier_12928.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01171944, version 1
  • OATAO : 12928

Collections

Citation

Mathieu Serrurier, Henri Prade. A scalable learning algorithm for Kernel Probabilistic Classifier. 7th International Conference on Scalable Uncertainty Management (SUM), Sep 2013, Washington, DC, United States. pp. 297-310. ⟨hal-01171944⟩

Share

Metrics

Record views

91

Files downloads

113