Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets

M Schneider, L Wang, C Marr - … and Machine Learning–ICANN 2019: Deep …, 2019 - Springer
Most machine learning algorithms require that training data are identically distributed to
ensure effective learning. In biological studies, however, even small variations in the …
被引用次数：2 相关文章

Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets

M Schneider, L Wang, C Marr - 2019 - europepmc.org
Most machine learning algorithms require that training data are identically distributed to
ensure effective learning. In biological studies, however, even small variations in the …
[PDF] archive.org

[PDF][PDF] Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets

M Schneider, L Wang, C Marr - scholar.archive.org
Most machine learning algorithms require that training data are identically distributed to
ensure effective learning. In biological studies, however, even small variations in the …

Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets

M Schneider, L Wang, C Marr - bioRxiv, 2019 - biorxiv.org
Most machine learning algorithms require that training data are identically distributed to
ensure effective learning. In biological studies, however, even small variations in the …

Evaluation of Domain Adaptation Approaches for Robust Classification of Heterogeneous Biological Data Sets

M Schneider, L Wang, C Marr - International Conference on Artificial …, 2019 - dl.acm.org
Most machine learning algorithms require that training data are identically distributed to
ensure effective learning. In biological studies, however, even small variations in the …
[PDF] archive.org

[PDF][PDF] Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets

M Schneider, L Wang, C Marr - scholar.archive.org
Most machine learning algorithms require that training data are identically distributed to
ensure effective learning. In biological studies, however, even small variations in the …