Multi-layer neural networks that use training algorithms based on statistical Bayesian inference. BNNs offer a number of important advantages over the standard Backpropagation learning algorithm including confidence intervals can be assigned to the predictions generated by a network they allow the values of regularization coefficients to be selected using only training data similarly, they allow different models to be compared using only the training data dealing with the issue of model complexity without the need to use cross validation
Published in Chapter:
Hierarchical Neuro-Fuzzy Systems Part I
Marley Vellasco (PUC-Rio, Brazil), Marco Pacheco (PUC-Rio, Brazil), Karla Figueiredo (UERJ, Brazil), and Flavio Souza (UERJ, Brazil)
Copyright: © 2009
|Pages: 9
DOI: 10.4018/978-1-59904-849-9.ch120
Abstract
Neuro-fuzzy [Jang,1997][Abraham,2005] are hybrid systems that combine the learning capacity of neural nets [Haykin,1999] with the linguistic interpretation of fuzzy inference systems [Ross,2004]. These systems have been evaluated quite intensively in machine learning tasks. This is mainly due to a number of factors: the applicability of learning algorithms developed for neural nets; the possibility of promoting implicit and explicit knowledge integration; and the possibility of extracting knowledge in the form of fuzzy rules. Most of the well known neuro-fuzzy systems, however, present limitations regarding the number of inputs allowed or the limited (or nonexistent) form to create their own structure and rules [Nauck,1997][Nauck,19 98][Vuorimaa,1994][Zhang,1995]. This paper describes a new class of neuro-fuzzy models, called Hierarchical Neuro-Fuzzy BSP Systems (HNFB). These models employ the BSP partitioning (Binary Space Partitioning) of the input space [Chrysanthou,1996] and have been developed to bypass traditional drawbacks of neuro-fuzzy systems. This paper introduces the HNFB models based on supervised learning algorithm. These models were evaluated in many benchmark applications related to classification and time-series forecasting. A second paper, entitled Hierarchical Neuro-Fuzzy Systems Part II, focuses on hierarchical neuro-fuzzy models based on reinforcement learning algorithms.