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Bing Liu is a Chinese-American professor of computer science who specializes in data mining, machine learning, and natural language processing.In 2002, he became a scholar at University of Illinois at Chicago. [1]
He served as the director of the Stanford Artificial Intelligence Laboratory (SAIL), where he taught students and undertook research related to data mining, big data, and machine learning. His machine learning course CS229 at Stanford is the most popular course offered on campus with over 1,000 students enrolling some years.
Jiawei Han – data mining; Frank Harary – graph theory; Brian Harris – machine translation research, Canada's first computer-assisted translation course, natural translation theory, community interpreting (Critical Link) Juris Hartmanis – computational complexity theory; Johan Håstad – computational complexity theory
Hastie is a prolific author of scientific works on various topics in applied statistics, including statistical learning, data mining, statistical computing, and bioinformatics. He along with his collaborators has authored about 125 scientific articles.
The methods must manage real-time data, diverse device types, and scale effectively. Garbe et al. [19] have introduced a multi-stage anomaly detection framework that improves upon traditional methods by incorporating spatial clustering, density-based clustering, and locality-sensitive hashing. This tailored approach is designed to better handle ...
Eduardo Schwartz, (born 1940), American, pioneering research in the real options method of pricing investments under uncertainty. Claude Shannon, (1916–2001), American, mathematician, electronic engineer, and cryptographer known as "the father of Information Theory".
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection [4] discusses the general pattern in various local outlier detection methods (including, e.g., LOF, a simplified version of LOF and LoOP) and abstracts from this into a general framework. This framework is then ...
First, the statistician may remove the suspected outliers from the data set and then use the arithmetic mean to estimate the location parameter. Second, the statistician may use a robust statistic, such as the median statistic. Peirce's criterion is a statistical procedure for eliminating outliers.