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Tom Michael Mitchell (born August 9, 1951) is an American computer scientist and the Founders University Professor at Carnegie Mellon University (CMU). He is a founder and former chair of the Machine Learning Department at CMU. [4]
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."
Thus, during learning, the version space (which itself is a set – possibly infinite – containing all consistent hypotheses) can be represented by just its lower and upper bounds (maximally general and maximally specific hypothesis sets), and learning operations can be performed just on these representative sets.
Co-training is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its uses is in text mining for search engines. It was introduced by Avrim Blum and Tom Mitchell in 1998.
CPL is an approach to semi-supervised learning that yields more accurate results by coupling the training of many information extractors. Basic idea behind CPL is that semi-supervised training of a single type of extractor such as ‘coach’ is much more difficult than simultaneously training many extractors that cover a variety of inter-related entity and relation types.
Never-Ending Language Learning system (NELL) is a semantic machine learning system that as of 2010 was being developed by a research team at Carnegie Mellon University, and supported by grants from DARPA, Google, NSF, and CNPq with portions of the system running on a supercomputing cluster provided by Yahoo!.
Heuristic Dendral "would serve as a template for similar knowledge-based systems in other areas" rather than just concentrating in the field of organic chemistry. Meta-Dendral was a model for knowledge-rich learning systems that was later codified in Tom Mitchell's influential Version Space Model of learning. [1]
Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous regions. [1] MoE represents a form of ensemble learning.