title: Dynamic probability estimator for machine learning.
abstract: An efficient algorithm for dynamic estimation of probabilities without division on unlimited number of input data is presented. The method estimates probabilities of the sampled data from the raw sample count, while keeping the total count value constant. Accuracy of the estimate depends on the counter size, rather than on the total number of data points. Estimator follows variations of the incoming data probability within a fixed window size, without explicit implementation of the windowing technique. Total design area is very small and all probabilities are estimated concurrently. Dynamic probability estimator was implemented using a programmable gate array from Xilinx. The performance of this implementation is evaluated in terms of the area efficiency and execution time. This method is suitable for the highly integrated design of artificial neural networks where a large number of dynamic probability estimators can work concurrently.
authors.affiliation: School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA. Starzyk@bobcat.ent.ohiou.edu
authors.forename: Janusz A
authors.initials: JA
authors.lastname: Starzyk
authors.forename: Feng
authors.initials: F
authors.lastname: Wang
mesh_terms: D001185:Artificial Intelligence; D011338:Probability Theory