Computer Science – Neural and Evolutionary Computing
Scientific paper
2007-04-20
Computer Science
Neural and Evolutionary Computing
8 pages, 4 figures, submitted for consideration to the "Statistics and Its Interface" journal
Scientific paper
The random initialization of weights of a multilayer perceptron makes it possible to model its training process as a Las Vegas algorithm, i.e. a randomized algorithm which stops when some required training error is obtained, and whose execution time is a random variable. This modeling is used to perform a case study on a well-known pattern recognition benchmark: the UCI Thyroid Disease Database. Empirical evidence is presented of the training time probability distribution exhibiting a heavy tail behavior, meaning a big probability mass of long executions. This fact is exploited to reduce the training time cost by applying two simple restart strategies. The first assumes full knowledge of the distribution yielding a 40% cut down in expected time with respect to the training without restarts. The second, assumes null knowledge, yielding a reduction ranging from 9% to 23%.
Cantador Ivan
Cebrian Manuel
No associations
LandOfFree
Exploiting Heavy Tails in Training Times of Multilayer Perceptrons: A Case Study with the UCI Thyroid Disease Database does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Exploiting Heavy Tails in Training Times of Multilayer Perceptrons: A Case Study with the UCI Thyroid Disease Database, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Exploiting Heavy Tails in Training Times of Multilayer Perceptrons: A Case Study with the UCI Thyroid Disease Database will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-573778