Feature selection methods for big data bioinformatics: A survey from the search perspective
L Wang, Y Wang, Q Chang - Methods, 2016 - Elsevier
This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …
Prototype‐based models in machine learning
An overview is given of prototype‐based models in machine learning. In this framework,
observations, ie, data, are stored in terms of typical representatives. Together with a suitable …
observations, ie, data, are stored in terms of typical representatives. Together with a suitable …
Id4 downstream of Notch2 maintains neural stem cell quiescence in the adult hippocampus
Neural stem cells (NSCs) in the adult mouse hippocampal dentate gyrus (DG) are mostly
quiescent, and only a few are in cell cycle at any point in time. DG NSCs become …
quiescent, and only a few are in cell cycle at any point in time. DG NSCs become …
An early intestinal cancer prediction algorithm based on deep belief network
The incidence of colorectal cancer (colorectal cancer, CRC) in China has increased in
recent years, and its mortality rate has become one of the highest among all cancers. CRC …
recent years, and its mortality rate has become one of the highest among all cancers. CRC …
Robust microarray data feature selection using a correntropy based distance metric learning approach
V Vahabzadeh, MH Moattar - Computers in Biology and Medicine, 2023 - Elsevier
Classification of high-dimensional microarray data is a challenge in bioinformatics and
genetic data processing. One of the challenging issues of feature selection is the presence …
genetic data processing. One of the challenging issues of feature selection is the presence …
Stationarity of matrix relevance LVQ
We present a theoretical analysis of Learning Vector Quantization (LVQ) with adaptive
distance measures. Specifically, we consider generalized Euclidean distances which are …
distance measures. Specifically, we consider generalized Euclidean distances which are …
[图书][B] The Shallow and the Deep: A biased introduction to neural networks and old school machine learning
M Biehl - 2023 - research.rug.nl
Abstract The Shallow and the Deep is a collection of lecture notes that offers an accessible
introduction to neural networks and machine learning in general. However, it was clear from …
introduction to neural networks and machine learning in general. However, it was clear from …
Biomedical applications of prototype based classifiers and relevance learning
M Biehl - Algorithms for Computational Biology: 4th International …, 2017 - Springer
In this contribution, prototype-based systems and relevance learning are presented and
discussed in the context of biomedical data analysis. Learning Vector Quantization and …
discussed in the context of biomedical data analysis. Learning Vector Quantization and …
Differential expression analysis in RNA-seq data using a geometric approach
Although differential gene expression (DGE) profiling in RNA-seq is used by many
researchers, new packages and pipelines are continuously being presented as a result of …
researchers, new packages and pipelines are continuously being presented as a result of …
[PDF][PDF] Supervised Learning–An Introduction
M Biehl - Machine Learning Reports 01/2019, 2019 - techfak.uni-bielefeld.de
These notes present a selection of topics in the area of supervised machine learning. The
focus is on the discussion of methods and algorithms for classification tasks. Regression by …
focus is on the discussion of methods and algorithms for classification tasks. Regression by …