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Machine learning offers clear advantages.
Deep machine learning offers a comprehensive framework for extracting meaningful features from complex observations in an unsupervised manner.
Machine learning offers the prospect of automating this adaptation cycle, reducing the burden of domain specific tuning and reconciling the conflicting needs of generality and efficacy.
Machine Learning offers the possibility to classify single items in large data sets and to categorize these items with regard to the context they are in.
The field of machine learning offers a solution to this by "training" a computer to recognise parts of an image in the same way a human does, the computer should be able to classify a noisy image unattended, potentially faster than a human operator (dependent on the computer's processing power and memory) and presenting the same output when executed repeatedly on the same input.
Machine learning offers a general method to learn the most performant predictive model for a specific context, database, or ICU.
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Novel data analysis methodologies, such as machine learning, offer large potential for identifying relevant data for specific biological outcomes based on complex multidimensional datasets.
Genetic Programming (GP), a heuristic machine learning technique offers several advantages compared to other machine learning approaches and consequently may be a promising tool for biosensor data classification.
Another example comes from machine learning, which offers various algorithms (such as support vector machines) that can be used to separate outliers from the remaining points (James et al. 2014).
Multi-dimensional machine learning methodology offers an intuitively appealing and technically elegant way to investigate multiple associations between variables compared to more conventional multivariate statistical approaches (e.g. principal component and factor analyses).
However, machine learning can offer other benefits.
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