Skip to Main content Skip to Navigation
Conference papers

Machine Learning with Known Input Data Uncertainty Measure

Abstract : Uncertainty of the input data is a common issue in machine learning. In this paper we show how one can incorporate knowledge on uncertainty measure regarding particular points in the training set. This may boost up models accuracy as well as reduce overfitting. We show an approach based on the classical training with jitter for Artificial Neural Networks (ANNs). We prove that our method, which can be applied to a wide class of models, is approximately equivalent to generalised Tikhonov regularisation learning. We also compare our results with some alternative methods. In the end we discuss further prospects and applications.
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download

https://hal.inria.fr/hal-01496084
Contributor : Hal Ifip <>
Submitted on : Monday, March 27, 2017 - 11:01:46 AM
Last modification on : Tuesday, March 28, 2017 - 1:07:10 AM
Long-term archiving on: : Wednesday, June 28, 2017 - 1:16:31 PM

File

978-3-642-40925-7_35_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Wojciech Czarnecki, Igor Podolak. Machine Learning with Known Input Data Uncertainty Measure. 12th International Conference on Information Systems and Industrial Management (CISIM), Sep 2013, Krakow, Poland. pp.379-388, ⟨10.1007/978-3-642-40925-7_35⟩. ⟨hal-01496084⟩

Share

Metrics

Record views

241

Files downloads

2244