entropy is exactly the same as the negative log likelihood (these were two concepts that were originally developed independently in the field of computer science and statistics, and they are motivated differently, but it turns out that they compute excactly the same in our classification context.)...
To highlight the generalizability of the framework, GaNDLF was applied on both radiology and histology data for a variety of DL workloads/tasks (i.e., segmentation, regression, and classification) on multiple organ systems, imaging modalities, and various applications using numerous DL architectures...
Cross entropy and mean squared error are typical cost functions used to optimize classifier performance. The goal of the optimization is usually to achieve the best correct classification rate. However, for many two-class real-world problems, the ROC curve is a more meaningful performance measure....
A stacking ensemble deep learning approach to cancer type classification based on TCGA data Article Open access 02 August 2021 Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer Article Open access 21 October 2024 Introduction...
TensorFlow is more of a low-level library; basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas scikit-learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs...
Microbiome-based classification We evaluated the microbiome-based classification capabilities for oCRC and yCRC using two widely adopted algorithms: random forest and least absolute shrinkage and selection operator (LASSO) logistic regression. The random forest algorithm, known for its superior performance ...
Section 3 is the introduction and derivation of the method. The conclusions of experiments and prospects for future work are presented in Sections 4 Experiments, 5 Conclusion, respectively. 2. Related work The detection of fake news has many related tasks, such as rumor detection (Cao et al.,...
4.1.1. Dataset and Data Allocation Method In order to show the application of the classification task, the MNIST dataset is selected as the local dataset source, and a two-layer MLP model is used as the training model for FL. The samples of each category were randomly sampled from the tra...
It is well-known that each new video coding standard significantly increases in computational complexity with respect to previous standards, and this is particularly true for the HEVC and VVC video coding standards. The development of techniques for reducing the required complexity without affecting the...
The cross-dataset evaluation supports OnClass as a robust method for automated cell type classification in datasets with large numbers of unseen cell types. Fig. 4: Training with different datasets and proportions of unseen cell types highlights OnClass versatility and accuracy. a–d Heatmaps ...