Elements of Machine Learning

By Pat Langley. 1995

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Huan Liu
2. J. Kolter
3. Cynthia Rudin
4. Reza Zafarani
5. Jie Cai
6. Yoshua Bengio
7. J. Ramon
8. M. Maloof

Citations in the corpus

• Functional genomic hypothesis generation and experimentation by a robot scientist (2004, background): “Machine learning is the branch of artificial intelligence that seeks to develop computer systems that improve their performance automatically with experienc”
• A systematic analysis of performance measures for classification tasks (2009, background): “First comprehensive books on Machine Learning were published in late 1990's (Langley, 1996; Mitchell, 1997).”
• Tagging gene and protein names in biomedical text (2002, methodology): “In order to take advantage of the general context of all the words in a document we use Na¨ıve Bayes learning (Langley, 1996; Mitchell, 1997; Wilbur, 2000) to predict the overall likelihood that a document contains a gene name.”
• Machine learning techniques to examine large patient databases. (2009, background): “28 A computer program is said to learn from experience if its performance at certain tasks improves with experience.”
• Learning classifier systems: a complete introduction, review, and roadmap (2009, unknown): “In the context of artificial intelligence, learning can be defined as, “the improvement of performance in some environment through the acquisition of knowledge resulting from experience in that environment” [12].”
• Dynamic weighted majority: a new ensemble method for tracking concept drift (2003, methodology): “The Streaming Ensemble Algorithm (sea) [4] copes with concept drift with an ensemble of c4.5 classiers [ 25 ].”
• Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner (2001, methodology): “The original RRL was tested on the blocks world [8] with three di(cid:11)erent goals: unstacking all blocks, stacking all blocks and stacking two speci(cid:12)c blocks.”
• Relational reinforcement learning (2001, unknown): “The blocks world [ 7 ] is used as a test-case, because it is easy to understand and it exhibits some characteristics that demonstrate some specific RRL advantages.”
• Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets (2010, background): “The strong showing of DOCMP for additive rules ( q = 4) is a topic worth further exploration.”
• Learning in multiagent systems (1999, background): “The reader interested in textbooks on single-agent learning is referred to [ 28 ] and [34].”