@inproceedings{b2bc5316e1994f5ea331a6394a8684ee,

title = "Tight bounds on the size of neural networks for classification problems",

abstract = "This paper relies on the entropy of a data-set (i.e., number-of-bits) to prove tight bounds on the size of neural networks solving a classification problem. First, based on a sequence of geometrical steps, we constructively compute an upper bound of O(mn) on the number-of-bits for a given dataset- here m is the number of examples and n is the number of dimensions (i.e., IR n). This result is used further in a nonconstructive way to bound the size of neural networks which correctly classify that data-set.",

keywords = "Boolean functions, Classification problems, Entropy, Neural networks, Size complexity",

author = "Valeriu Beiu and {De Pauw}, Thierry",

year = "1997",

doi = "10.1007/bfb0032533",

language = "English",

isbn = "3540630473",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "743--752",

booktitle = "Biological and Artificial Computation",

note = "4th International Work-Conference on Artificial and Natural Neural Networks, IWANN 1997 ; Conference date: 04-06-1997 Through 06-06-1997",

}