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The effect of numeric features on the scalability of inductive learning programs
[chapter]
1995
Lecture Notes in Computer Science
The behaviour of a learning program as the quantity of data increases affects to a large extent its applicability on real-world problems. This paper presents the results of a theoretical and experimental investigation of the scalability of four well-known empirical concept learning programs. In particular it examines the effect of using numeric features in the training set. The theoretical part of the work involved a detailed worst-case computational complexity anMysis of the algorithms. The
doi:10.1007/3-540-59286-5_60
fatcat:qupbcpq7qrh6tgenfabfmag43q