Learning Efficient Classification Procedures and Their Application to Chess End Games

By J. Ross Quinlan. 1983

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Citations in the corpus

Scalable Bayesian Rule Lists (2016, methodology): “Rule learning algorithms have been developed possibly since the AQ algorithm in the 1960s (Michalski, 1969) and throughout the 1980’s and 90’s (Cendrowska, 1987; Quinlan, 1983; Clark & Niblett, 1989; Cohen, 1995; Liu, Hsu, & Ma, 1998).”
Induction of Decision Trees (1986, background): “ID3 (Quinlan, 1979, 1983a) is one of a series of programs developed from CLS in response to a challenging induction task posed by Donald Michie, viz. to decide from pattern-based features alone whether a particular chess position in the King-Rook vs King-Knight endgame is lost for the Knight's side…”
Layered Concept-Learning and Dynamically Variable Bias Management (1987, background): “A very grainy algorithm space might contain a few fixed concept learning systems, such as AQ, ID3, PLS1, etc. (Michalski, 1983; Quinlan, 1983; Rendell, 1983).”
Practical Issues in Temporal Difference Learning (1991, background): “…chess, Othello and backgammon have been widely studied as testing grounds for various machine learning procedures (Samuel, 1959; Samuel, 1967; Griffith, 1974; Quinlan, 1983; Mitchell, 1984; Frey, 1986; Christensen & Korf, 1986; Lee & Mahajan, 1988; Tesauro & Sejnowski, 1989; Tesauro, 1990).”
Connectionist expert systems (1988, methodology): “Quinlan’s ID3 algorithm [ 32 ], a decision-tree-based method that is the basis for several commercially available packages.”
Incremental Induction of Decision Trees (1989, methodology): “Training instances for the Quinlan (1983) example. class height hair eyes – short blond brown – tall dark brown + tall blond blue – tall dark blue – short dark blue + tall red blue – tall blond brown + short blond blue”
A System for Induction of Oblique Decision Trees (1994, background): “In the 1980's, Breiman et al.'s book on classi(cid:12)cation and regression trees (CART) and Quin-lan's work on ID3 (Quinlan, 1983, 1986) provided the foundations for what has become a large body of research on one of the central techniques of experimental machine learning.”
Separate-and-Conquer Rule Learning (1999, methodology): “Originating from the ID3 decision tree learning system (Quinlan 1983), this measure has been used in early versions of the CN2 learning algorithm (Clark and Niblett 1989).”
A Taxonomy of Recommender Agents on the Internet (2003, methodology): “The most widely-used decision tree learner applied to proﬁling is the ID3 (Quinlan 1983).”
The CN2 induction algorithm (2004, methodology): “Two families of systems, based on the ID3 (Quinlan, 1983) and AQ (Michalski, 1969) algorithms, have been especially successful.”