Cross- and autocorrelation analysis showed that estimates from the first and second 10-min samples were similar (r = 0.65) and variation between them (i.e., within the hour) was random, supporting the conclusion that systematic-hourly sampling is a defensible sampling design for acoustic enumeration when temporal variation in fish migration is unknown a priori.
In addition, multiple peaks in the carbonyl region of their C NMR spectra indicated that these hydrophobic blocks were random (Supporting Information).
In summary, the observation that the methods agree with each other more often than expected by random supports the notion that codon reading can be detected from codon usage bias.
Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance.
The mean square stability of a gyropendulum under random vertical support excitation is considered.
Random Forest, Support Vector Machines and Artificial Neural Networks algorithms were trained to predict bat species from acoustic variables.
The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor).
Then, the researcher trains various different regressor model types (random forests, support vector machines, neural networks and multiple linear regression).
In this work, we have selected three well known machine learning algorithms: random forests, support vector machines, and naive Bayes.
The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network.
