Recent years have witnessed an increasing interest in the use of machine learning techniques for categorizing information. Computers have demonstrated a recognition rate better than or comparable to human performance in several tasks, such as visual recog
One of the goals of artificial intelligence and machine learning is to allow computers to understand
our world. A crucial aspect to achieve this goal is to quantify uncertainty but - so far - that has been
been largely neglected in machine learning. Bayesian methodologies incorporate uncertainty in a
natural way. While they receive more and more attention, they still lack the accuracy or
performance that one has come to ...
In this age of ubiquitous digital interconnectivity, we may envisage that humans will increasingly
delegate their social, economic or data-related transactions to an autonomous agent, for reasons of
convenience or complexity. Although the scientific knowledge to create such systems appears to be
available, this transformation does not appear to become commonplace soon, except maybe the
use of basic digital assistants. ...
An algorithm, like a human, is said to learn if its performance on a task improves as it gains more experience. For example, consider the task of labeling an abnormality on a mammogram as benign or malignant, where experience consists of a radiologist’s description of an abnormality and its known label. Traditional machine learning approaches for binary classification require having access to fully labeled data, containing positive and ...
Modeling and automating business processes is important for improving efficiency and effectiveness in businesses. During the execution of a business process, a plethora of business decisions need to be made in order to ensure the correct enactment of the business process. With increasing automation of business processes, the need for the automation of routine business decisions grows, as business processes can contain a lot of decisions or ...
While automatic emotion detection in text is still in its infancy, machine learning techniques are currently considered as a state-of-the-art methodology to identify emotional states in text. These systems, however, rely on large quantities of annotated data from which prediction models are derived, so progress is mainly made for majority languages such as English. As the systemU+2019s performance is dependent on the kind of data it is ...
The sensor monitoring systems of today can detect anomalous behaviour & derive their underlying causes by either using expert-driven rules or by data-driven machine learning models. Expert-driven approaches require much human involvement to operate in environments providing the expert information on the fly. In contrast, data-driven approaches are more adaptive to specific changes but require large amounts of data to generalise well & ...
Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a communication
channel that does not rely on muscular control. BCIs usually rely on EEG signals acquired from the
subject's scalp or on electrophysiological signals from brain implants. The latter yield a superior
decoding performance, but as the implant damages the cortical tissue, long-term signal stability is a
concern. EEG does not ...