Subject
Creator
- Bartlomiej P. Rajwa 2
- Murat Dundar 2
- Baichuan Zhang 1
- Ferit Akova 1
- Halid Ziya Yerebakan 1
- Qiang Kou 1
- Yicheng He 1
Type
- Dataset 2
Date
subject: Computer Science subject: Biomedical Engineering type: dataset
Total is 2 Results
Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data
10.4231/R7N58J9Z
Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He
10/01/2015
A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity
bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning
A Non-parametric Bayesian Model for Joint Cell Clustering and Cluster Matching: Identification of Anomalous Sample Phenotypes with Random Effects.
10.4231/R7KK98PG
Bartlomiej P. Rajwa , Ferit Akova , Halid Ziya Yerebakan , Murat Dundar
09/03/2014
The manuscript presents a non-parametric Bayesian algorithm called ASPIRE (Anomalous Sample Phenotype Identification with Random Effects) able to identify phenotypic differences across batches of cytometry samples in the presence of random effects
AML Bayesian Biomedical Engineering BMC Bioinformatics Computer Science cytometry Dirichlet process Gaussian mixture model Interdisciplinary Research Life Sciences random effects
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