PARAMETRIC LINK MODELS FOR KNOWLEDGE TRANSFER IN STATISTICAL LEARNING

Farid Beninel (1) , Christophe Biernacki (2, 3) , Charles Bouveyron (4) , Julien Jacques (2, 3) , Alexandre Lourme (2, 3)

1 IUT Departement STID
2 MODAL - MOdel for Data Analysis and Learning
3 LPP - Laboratoire Paul Painlevé - UMR 8524
4 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5

When a statistical model is designed in a prediction purpose, a major assumption is the absence of evolution in the modeled phenomenon between the training and the prediction stages. Thus, training and future data must be in the same feature space and must have the same distribution. Unfortunately, this assumption turns out to be often false in real-world applications. For instance, biological motivations could lead to classify individuals from a given species when only individuals from another species are available for training. In regression, we would sometimes use a predictive model for data having not exactly the same distribution that the training data used for estimating the model. This chapter presents techniques for transfering a statistical model estimated from a source population to a target population. Three tasks of statistical learning are considered: Probabilistic classification (parametric and semi-parametric), linear regression (includingmixture of regressions) and model-based clustering (Gaussian and Student). In each situation, the knowledge transfer is carried out by introducing parametric links between both populations. The use of such transfer techniques would improve the performance of learning by avoiding much expensive data labeling efforts.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Farid Beninel, Christophe Biernacki, Charles Bouveyron, Julien Jacques, Alexandre Lourme. PARAMETRIC LINK MODELS FOR KNOWLEDGE TRANSFER IN STATISTICAL LEARNING. Dragan Ilic. Knowledge Transfer: Practices, Types and Challenges, Nova Publishers, 2012. ⟨hal-00942660⟩