El-Halees AM, "Arabic text classification using maximum entropy". IUG Journal of Natural Studies. 2015 Dec 5;15(1).A. El-Halees. Arabic text classification using maximum entropy. IUG Journal of Natural Studies, 15(1):157-167, 2015....
using System; namespace NeuralClassification { class NeuralProgram { static void Main(string[] args) { Console.WriteLine("Begin neural network demo"); Console.WriteLine("Goal is to predict species of Iris flower"); Console.WriteLine("Raw data looks like: "); Console.WriteLine("blue, 1.4, ...
The 8 gait outcomes (weights > 40) were the Root Means Square AP, V, Cross Entropy APV, MLV, step regularity V, Lyapunov exponent V, stride regularity V and The Index of Harmonicity V. Figure 3 (a) Age-classification results for young-middle aged, healthy older and geriatric ...
Multi-source transfer learning has been explored widely in text classification33, pattern recognition in EEG signals34, speech recognition35 etc. One of the approaches for multi-source transfer learning relies on the assumption that the target task can be represented as a weighted combination of the...
Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10, 175-189. [57] Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Hum.Genet....
Here, we will analyze the length of the reviews and the summary to get an overall idea about the distribution of length of the text.This will help us fix the maximum length of the sequence: Output: Interesting. We can fix the maximum length of the reviews to 80 since that seems to be...
loss = crossentropy(Y,T,ClassificationMode="multilabel"); gradients = dlgradient(loss,parameters);end Model Predictions Function ThemodelPredictionsfunction takes as input the model parameters, a word encoding, an array of tokenized documents, a mini-batch size, and a maximum sequence length, and...
Building upon these improvements, the DISFC model attained more precise and longer-term predictions for MCI conversion than existing methods. Predicting MCI conversion can be viewed as a challenging fine-grained classification task, characterized by slight inter-class differences and high intra-class ...
A binary classification (positive and negative labeling) may be used to predict stability. However, such positive and negative learning cannot be used to predict the synthesizability as there is no negative (“unsynthesizable”) crystal data, since the inability to synthesize a hypothetical crystal...
One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelera