The data representativeness criterion: Predicting the performance of supervised classification based on data set similarity.

Autor: Evelien Schat, Rens van de Schoot, Wouter M Kouw, Duco Veen, Adriënne M Mendrik

Publikováno v: PLoS ONE, Vol 15, Iss 8, p e0237009 (2020)

In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when t

Externí odkaz: https://doaj.org/article/195e8c17af5c4237bd5689ba45eb6eef

Message Passing-Based Inference for Time-Varying Autoregressive Models

Autor: Albert Podusenko, Wouter M. Kouw, Bert de Vries

Publikováno v: Entropy, Vol 23, Iss 6, p 683 (2021)

Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by

Externí odkaz: https://doaj.org/article/e46dbd760dbe4d8fafdf60162159939f

A Review of Domain Adaptation without Target Labels

Autor: Marco Loog, Wouter M. Kouw

Publikováno v: Kouw, W M & Loog, M 2021, ' A Review of Domain Adaptation without Target Labels ', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 3, 8861136, pp. 766-785 . https://doi.org/10.1109/TPAMI.2019.2945942

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approach

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::989d843c5568c464861c6e01f048f0b0
https://doi.org/10.1109/tpami.2019.2945942

On Epistemics in Expected Free Energy for Linear Gaussian State Space Models

Autor: Wouter M. Kouw, Magnus T. Koudahl, Bert de Vries

Publikováno v: Entropy
Entropy; Volume 23; Issue 12; Pages: 1565
Entropy, 23(12):1565. Multidisciplinary Digital Publishing Institute (MDPI)
Entropy, Vol 23, Iss 1565, p 1565 (2021)

Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such as the human brain, and to design synthetic intelligent systems (agents). In this paper we show that Expected Free E

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b76e14fd76642b401a501fb369aa53a
https://doi.org/10.3390/e23121565

Message Passing-Based Inference for Time-Varying Autoregressive Models

Autor: Wouter M. Kouw, Bert de Vries, Albert Podusenko

Publikováno v: Entropy, Vol 23, Iss 683, p 683 (2021)
Entropy
Volume 23
Issue 6
Entropy, 23(6):683. Multidisciplinary Digital Publishing Institute (MDPI)

Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::312fac67578b4d316fbe4436d097cf00
https://www.mdpi.com/1099-4300/23/6/683