An approach is provided for training classifiers used in machine learning. A corpus of training data is received. One or more clusters of the training data is generated according to features of the training data. The one or more clusters are refined using user-specified rules. One or more ...
In this study, we investigate how an organism’s codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build several AI/machine learning models. When trained on...
Saeh, I. S., Mustafa, M. W., Machine Learning Classifiers for Steady State Security Evaluation in Power System. International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012.I. Saeh and M.W.Mustafa, "Machine learning classifiers for steady state security evaluation...
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that e...
Performance evalua- tion of machine learning classifiers in sentiment mining. International Journal of Computer Trends and Technology. 2013; 4(6):1783-6.Vinodhini G, Chandrasekaran R. Performance Evaluation of Machine Learning Classifiers in Sentiment Mining. International Journal of Computer Trends and...
The first seven numeric values on each line are the predictor values, often called attributes or features in machine learning terminology. The predictors are seed area, perimeter, compactness, length, width, asymmetry coefficient, and groove length. The item-to-predict (often ...
[Machine Learning] Deep Neural Network Classifiers Using CNTK By James McCaffrey The Microsoft Cognitive Toolkit (CNTK) library is a powerful set of functions that allows you to create machine learning (ML) prediction systems. I provided an introduction to version 2 in the J...
needing any expertise in machine learning. For instance, a custom classifier can be built to classify loan contracts, invoices, and project documents. Together, both built-in and build-your-own trainable classifiers provide classification support for a breadth of catego...
Benchmark datasets are the inevitable tool required to scrutinize vulnerabilities and tools in network security. Current datasets lack correlation between normal and the real-time network traffic. Behind every evaluation and establishment of attack detec
six models were produced using three different machine-learning algorithms (see Methods); three were trained on a single docked pose for each ligand, and three were trained on multiple RMSD-labelled poses for each ligand. In addition, single and multi-pose consensus models were produced by taking...