An experimental comparison of symbolic and neural learning algorithms

In this paper comparative strengths and weaknesses of symbolic and neural learning algorithms are analysed. Experiments comparing the new generation symbolic algorithms and neural network algorithms have been performed using twelve large, real-world data sets.


A neural network approach for approximate force response analyses of a bridge population
Hasançebi, Oğuzhan (2013-03-01)
In this paper, artificial neural networks (ANNs) are used to develop an efficient method for rapid and approximate force response analyses of a bridge population. The single-span reinforced concrete T-beam bridge population in Pennsylvania State is taken as a particular case study. First, a statistical analysis is conducted to examine implicit and explicit dependencies between various geometrical and structural parameters of the bridges, and the governing bridge parameters are identified along with their ra...
On the analysis of deep convolutional neural networks applied to building detection in satellite images
Karagöz, Batuhan; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2015)
Deep Learning has gained much interest recently, probably induced by the re- quirements to learn more complex and abstract concepts. As concepts to be learned become more abstract, their regions in the raw input space also become highly variational. In many cases, shallow architectures fail to learn highly varia- tional functions. One area of interest where concepts to be learned are complex is remote sensing. In this thesis, performance and suitability of deep architectures for recognition of building patc...
A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
A systematic study of probabilistic aggregation strategies in swarm robotic systems
Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005)
In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...
Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks
Kocamaz, Korhan; Binici, Barış; Tuncay, Kağan (2021-09-08)
Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensiv...
Citation Formats
N. Baykal, “An experimental comparison of symbolic and neural learning algorithms,” 1998, Accessed: 00, 2020. [Online]. Available: