Machine Learning (ML) Algorithm

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A Machine Learning (ML) Algorithm is a learning algorithm that is an automated 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.


  • (Mitchell, 2006) ⇒ Tom M. Mitchell (2006). “The Discipline of Machine Learning." Machine Learning Department technical report CMU-ML-06-108, Carnegie Mellon University.
    • "Machine Learning research asks “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” ...
    • "Can machine learning theories and algorithms help explain human learning? ...
    • "What is the relationship between different learning algorithms, and which should be used when?. Many different learning algorithms have been proposed and evaluated experimentally in different application domains. One theme of research is to develop a theoretical understanding of the relationships among these algorithms, and of when it is appropriate to use each. For example, two algorithms for supervised learning, Logistic Regression and the Naive Bayes classifier, behave differently on many data sets, but can be proved to be equivalent when applied to certain types of data sets. ...


  • (Dumais et al., 1998) ⇒ Susan Dumais, John Platt, David Heckerman, and Mehran Sahami. (1998). “Inductive Learning Algorithms and Representations for Text Categorization.” In: Proceedings of the seventh International Conference on Information and knowledge management (CIKM 1998) doi:10.1145/288627.288651