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References in periodicals archive ?
In fact, some machine learning systems are getting so good that they're actually bumping up against the human-caused flaws in the training data.
I have at times heard that joy breaks out among developers of machine learning systems.
"Nearly all operational systems-such as manufacturing lines, industrial equipment, data centers and handheld devices-produce streams or bursts of time series data in the form of sensor readings, log entries or activity traces," says Nikunj Mehta, Ph.D., CEO and founder of Falkonry, a supplier of machine learning systems and predictive analytics technologies.
For example, a diagnostic machine learning system might argue that a patient probably has pneumonia, because he is a male and he is coughing.
Once a target recalibrates its system to filter out the false alarms, the attacker can launch a real attack that can get by the machine learning system.
"But when the architecture of Google Translate was subjected to a machine learning system, it yielded a totally different result; one which today can absorb the meaning from any language into a central language neutral representation of ideas, which, in turn, can be expressed in any other language.
It is logical that the more number of examples of spam and non-spam emails the Machine Learning system goes through, the better will be its prediction for the next unknown email.
They trained a machine learning system to make dermatologic diagnoses from photos of skin lesions.
In supervised machine learning, humans label the data in the system, and the system then uses algorithms to detect correlations and patterns linking various pieces of information--"rules" which can be applied to data outside of the labeled set.(188) A supervised machine learning system can be used to classify individuals within a dataset.(189) For example, a human user might label the professions of individuals, based on information obtained from surveys, within a dataset containing other information such as age, education, ethnicity, subscription history, internet browsing history, and location.
The machine learning system collects data on the user, say [x.sub.t], such as Facebook shares, comments, status updates, and spending patterns, which is a function of his/her preferences [p.sub.t] and the side information s, given by
Potential competitors may be financial companies which are our clients as of now, if they try to build their own machine learning system.
To accomplish this we rely on a Bayesian inferential framework where a machine learning system considers a set of hypotheses about its surroundings and identifies more probable hypotheses given incoming sensor data.
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