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Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection: 2016 (possibly updated with new datasets and/or results) [331] Campos et al.
The University of California Irvine hosts the UCI Machine Learning Repository, a data resource which is very popular among machine learning researchers and data mining practitioners. [97] It was created in 1987 and contains 622 datasets from several domains including biology, medicine, physics, engineering, social sciences, games, and others. [98]
The following tree was constructed using JBoost on the spambase dataset [3] (available from the UCI Machine Learning Repository). [4] In this example, spam is coded as 1 and regular email is coded as −1. An ADTree for 6 iterations on the Spambase dataset. The following table contains part of the information for a single instance.
UCI Machine Learning Repository Content Summary (See "Pima Indians Diabetes Database" for the original data set of 732 records, and additional notes.) MATLAB code for one dimensional and two dimensional density estimation; libAGF C++ software for variable kernel density estimation
Repository dedicated to sport and exercise related research 279 2017 Center for Open Science: SSRN (First Look) Multidisciplinary: Aggregates over 30 preprint servers (Preprints with The Lancet, Cell Sneak Peek, etc.). More than 55 disciplines. Initially funded by SSRN in 1994, bought in 2016 by Elsevier. 950,733 1994 Elsevier: TechRxiv ...
She was made an assistant professor with tenure at UC Irvine in 2016. [7] She specialised in large-scale machine learning and high-dimensional statistics. [8] Anandkumar was a Principal Scientist at Amazon Web Services from 2016 to 2018. [9]
Towana Looney is the only person in the world living with a functional pig kidney. But her doctor predicts that in less than a decade, pig-to-human organ transplants like hers could become routine.
Rina Dechter (born August 13, 1950) is a distinguished professor of computer science [1] in the Donald Bren School of Information and Computer Sciences at the University of California, Irvine. Her research is on automated reasoning in artificial intelligence focusing on probabilistic and constraint-based reasoning .