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This article about a computer science journal is a stub. You can help Wikipedia by expanding it. See tips for writing articles about academic journals. Further suggestions might be found on the article's talk page.
Larry Alan Wasserman (born 1959) is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University. Biography
Michalski was born in Kalusz near Lvov on 7 May 1937. He received an equivalent of Bachelor of Science degree in Electrical Engineering at the Universities of Technology in Kraków and Warsaw in 1959; obtained M.S. Computer Science at the Polytechnic Institute of St. Petersburg in 1961; and Ph.D. in Computer Science at the Silesian University of Technology, Gliwice in 1969. In the period 1962 ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The Expectation Maximization Algorithm (PDF) (Technical Report number GIT-GVU-02-20). Georgia Tech College of Computing. gives an easier explanation of EM algorithm as to lowerbound maximization. Bishop, Christopher M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN 978-0-387-31073-2. Gupta, M. R.; Chen, Y. (2010).
Decision trees are a popular method for various machine learning tasks. Tree learning is almost "an off-the-shelf procedure for data mining", say Hastie et al., "because it is invariant under scaling and various other transformations of feature values, is robust to inclusion of irrelevant features, and produces inspectable models.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...