Statistical Machine Learning

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See: Statistical Machine Learning Algorithm.




  • (Sarawagi, 2008) ⇒ Sunita Sarawagi. (2008). “Information Extraction.” In: Foundations and Trends in Databases, 1(3).
    • ... We described Conditional Random Fields, a state-of-the-art method for entity recognition that imposes a joint distribution over the sequence of entity labels assigned to a given sequence of tokens. Although the details of training and inference on statistical models are somewhat involved for someone outside the field of statistical machine learning, the models are easy to deploy and customize due to their fairly nonrestrictive feature based framework.


    • Statistical machine learning merges statistics with the computational sciences --- computer science, systems science and optimization. Much of the agenda in statistical machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamical and heterogeneous, and where mathematical and algorithmic creativity are required to bring statistical methodology to bear. Fields such as bioinformatics, artificial intelligence, signal processing, communications, networking, information management, finance, game theory and control theory are all being heavily influenced by developments in statistical machine learning.
    • The field of statistical machine learning also poses some of the most challenging theoretical problems in modern statistics, chief among them being the general problem of understanding the link between inference and computation.