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Hyperspectral data processing was carried out applying first principal component analysis (PCA) for data exploration and then partial least square-discriminant analysis (PLS-DA) to build classification models.
Here, experimental results of the screening have been used to build classification models using SMF descriptors and ISIDA modeling tools (Naïve Bayes (NB) and SVM modules).
ACM is a data mining framework that employs association rule mining (ARM) methods to build classification systems, also known as associative classifiers.
To test every module of ChemSAR and to build a model with high prediction performance, we employed five methods (RF, SVM, k-NN, NB, DT) to build classification models.
However, when it comes to massive data, it is difficult for current data mining algorithms to build classification models with serial algorithm running on single machines, not to mention accurate models.
We approach this task in a standard supervised learning setting: we extract discriminative features from the sensor data and use state-of-the-art classifiers (SVM, Logistic Regression and Decision Tree Family) to build classification models.
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The environment we defined can also be used to gather similar project elements in order to build classifications of tasks, problems, arguments, etc. produced in a company.
For these datasets, we built classification models using support-vector machines (SVM) and Bayesian regularized neural networks (BRANN), trying several different descriptor sets.
After these models are built, classification of fault signal is accomplished by computing the conditional likelihoods of the signal under each learned GMM model and selecting the model with the highest likelihood.
Indeed, in later work (unpublished) in which we built classification models using later disease stages for the model development set, we did identify patterns strongly predictive of ovarian cancer.
We then built classification models that predict whether a given DNA repair gene is ageing-related or not.
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