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To address this issue, now Nature Machine Intelligence gives authors an option to publish open access papers for an additional fee, [3] and "authors remain owners of the research reported, and the code and data supporting the main findings of an article should be openly available. Moreover, preprints are allowed, in fact encouraged, and a link ...
The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition.
In machine learning, pattern recognition is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification , which attempts to assign each input value to one of a given set of classes (for example, determine whether a given ...
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology.It relates to connectionism, social behavior, and emergence.
Machine learning is a branch of statistics and computer science which studies algorithms and architectures that learn from observed facts. The main article for this category is Machine learning . Wikimedia Commons has media related to Machine learning .
During training, a node (or region) receives a temporal sequence of spatial patterns as its input. The learning process consists of two stages: The spatial pooling identifies (in the input) frequently observed patterns and memorise them as "coincidences". Patterns that are significantly similar to each other are treated as the same coincidence.
Cynthia Diane Rudin (born 1976) [1] is an American computer scientist and statistician specializing in machine learning and known for her work in interpretable machine learning. She is the director of the Interpretable Machine Learning Lab at Duke University , where she is a professor of computer science, electrical and computer engineering ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]