Skip to Main content Skip to Navigation
New interface
Conference papers

Linear Probability Forecasting

Abstract : In this paper we consider two online multi-class classification problems: classification with linear models and with kernelized models. The predictions can be thought of as probability distributions. The quality of predictions is measured by the Brier loss function. We suggest two computationally efficient algorithms to work with these problems, the second algorithm is derived by considering a new class of linear prediction models. We prove theoretical guarantees on the cumulative losses of the algorithms. We kernelize one of the algorithms and prove theoretical guarantees on the loss of the kernelized version. We perform experiments and compare our algorithms with logistic regression.
Document type :
Conference papers
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, November 16, 2017 - 3:56:04 PM
Last modification on : Thursday, March 5, 2020 - 5:43:04 PM
Long-term archiving on: : Saturday, February 17, 2018 - 3:46:41 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Fedor Zhdanov, Yuri Kalnishkan. Linear Probability Forecasting. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. pp.4-11, ⟨10.1007/978-3-642-16239-8_4⟩. ⟨hal-01060645⟩



Record views


Files downloads