Search results
Results from the WOW.Com Content Network
A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. [15] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. [16]
While at SRI International, Duda and Peter E. Hart were the authors of "Pattern Classification and Scene Analysis", originally published in 1973. This classic text is a widely cited reference, and the first edition was in print for over 25 years until being superseded by the second edition in 2000. [2] Duda is an IEEE Fellow and a AAAI Fellow. [3]
The Kernel-based Hough transform uses the same (,) parameterization proposed by Duda and Hart but operates on clusters of approximately collinear pixels. For each cluster, votes are cast using an oriented elliptical-Gaussian kernel that models the uncertainty associated with the best-fitting line with respect to the corresponding cluster.
In August 2003, Project Gutenberg created a CD containing approximately 600 of the "best" e-books from the collection. The CD is available for download as an ISO image. When users are unable to download the CD, they can request to have a copy sent to them, free of charge. In December 2003, a DVD was created containing nearly 10,000 items. At ...
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 ]
Hart was an author and his works are available free of charge on the Project Gutenberg server. He was also a member of the RepRap Project, which aims at creating a self-replicating machine. [6] Hart was involved in an early effort in 1993 to develop a free and openly accessible "Internet Encyclopedia", called "Interpedia". However, the effort ...
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 ...
Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering generic principles that allow a learning machine to be successful. For example, Bengio and LeCun (2007) wrote an article regarding local vs non-local learning, as well as shallow vs deep architecture. [231]