Based on such insights, learning objectives can be defined for cultural competence training targeted specifically at such care.
Based on such a conclusion, citizen learning experiences can be understood as their perceptions, responses, and performances to learning resources and learning approaches, in different fields in the city.
Then, with a view to prolonging network lifetime, it discusses the routing protocols based on such intelligent algorithms as reinforcement learning (RL), ant colony optimization (ACO), fuzzy logic (FL), genetic algorithm (GA), and neural networks (NNs).
Such experiments would be very much in the spirit of Wallach's earlier work on adaptation to visual cue conflict (e.g., Wallach & Frey, 1972; Wallach & Karsh, 1963; Wallach, Moore, & Davidson, 1963), as well as of the subsequent study of perceptual adaptation and learning based on such conflict (e.g., Fisher & Ebenholtz, 1986; Priot, Laboissière, Sillan, Roumes, & Prablanc, 2010).
The classifiers C1,…, C q are each learned by directly optimizing an objective function that is based on such conditional probabilities, calculated with respect to the training data.
As compared to other hyper-heuristics based on supervised learning such as decision tree [107, 127], logistic regression [57], support vector machine [129], and artificial neural networks [32, 138], genetic programming (GP) has shown a number of key advantages.
This study is focused on the high-performance fault predictors based on machine learning such as Random Forests and the algorithms based on a new computational intelligence approach called Artificial Immune Systems.
The prediction performances of the MGA and conventional gene-finding tools based on unsupervised learning, such as GeneMarkS and Glimmer3, were evaluated on various datasets.
Data modeling techniques based on machine learning such as support vector machines (SVMs) can partially reduce workload, aid clinical decision-making, and lower the frequency of human error [ 4].
Most of the existing tumor classification approaches are based on supervised learning, such as support vector machines or decision trees, which aimed at identifying genetic features to distinguish two or more known tumor (sub- types [ 49, 50].
