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- See: Machine Learning-based System, Predictive System.
- (Kumar et al., 2013) ⇒ Arun Kumar, Feng Niu, and Christopher Ré. (2013). “Hazy: Making It Easier to Build and Maintain Big-data Analytics.” In: Communications of the ACM Journal, 56(3). doi:10.1145/2428556.2428570
- QUOTE: Many state-of-the-art approaches to both of these challenges are largely statistical and combine rich databases with software driven by statistical analysis and machine learning. Examples include Google's Knowledge Graph, Apple's Siri, IBM's Jeopardy-winning Watson system, and the recommendation systems of Amazon and Netflix. The success of these big-data analytics–driven systems, also known as trained systems, has captured the public imagination, and there is excitement about bringing such capabilities to other applications in enterprises, health care, science, and government. The complexity of such systems, however, means that building them is very challenging, even for Ph.D.-level computer scientists. If such systems are to have truly broad impact, building and maintaining them needs to become substantially easier, so that they can be turned into commodities that can be easily applied to different domains. The research emphasis so far has been on individual algorithms for specific machine-learning tasks.
- (Kell & Oliver, 2004) ⇒ Douglas B Kell, and Stephen G Oliver. (2004). “Here is the Evidence, Now What is the Hypothesis? The Complementary Roles of Inductive and Hypothesis-driven Science in the Post-genomic Era.” In: Bioessays, 26(1). doi:10.1002/bies.10385