The Experts below are selected from a list of 5970 Experts worldwide ranked by ideXlab platform
Nikola Kasabov - One of the best experts on this subject based on the ideXlab platform.
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2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
Neural Networks, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
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quantum inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling
International Joint Conference on Neural Network, 2009Co-Authors: Stefan Schliebs, Susan P Worner, Michael Defoin Platel, Nikola KasabovAbstract:The paper introduces a framework and implementation of an integrated Connectionist System, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a Naive Bayesian Classifier.
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integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
International Joint Conference on Neural Network, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
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evolving Connectionist System based role allocation for robotic soccer
International Journal of Advanced Robotic Systems, 2008Co-Authors: L Huang, Q Song, Nikola KasabovAbstract:Robotic soccer is an intelligent System where a group of mobile robots are controlled to perform soccer play (http://www.fira.net). The allocation of a suitable role for each robot in a team is a key for the success of the play. The paper treats this issue as one of pattern classification, and solves it with an Evolving classification function (ECF), a special evolving Connectionist System (ECOS). A robot's role is determined by and evolves with the states of System (robots and target) in real time. The software and hardware platforms are set up for data collection and learning. The effectiveness of the proposed approach is verified by the experimental studies.
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evolving Connectionist System versus algebraic formulas for prediction of renal function from serum creatinine
Kidney International, 2005Co-Authors: Mark R Marshall, Nikola Kasabov, Qun Song, Tian Min, Stephen G MacdonellAbstract:Evolving Connectionist System versus algebraic formulas for prediction of renal function from serum creatinine. Background In clinical trials, equation 7 from the Modification of Diet in Renal Disease (MDRD) Study is the most accurate formula for the prediction of glomerular filtration rate (GFR) from serum creatinine. An alternative approach has been developed using evolving Connectionist Systems (ECOS), which are novel computing structures that can be trained to generate accurate output from a given set of input variables. This study aims to compare the prediction errors associated with each method, using data that reproduce routine clinical practice as opposed to the artificial setting of clinical trials. Methods The methods were compared using 441 radioisotope measurements of GFR in 178 chronic kidney disease patients from 12 centers in Australia and New Zealand. All clinical and laboratory measurements were obtained from the patients' center rather than central laboratories, as would be the case in routine clinical practice. Both the MDRD formula and ECOS used the same predictive variables, and both were optimized to the study cohort by stepwise regression and training, respectively. Results Mean measured GFR in the cohort was 22.6 mL/min/1.73 m 2 . The bias and precision of the MDRD formula were -3.5 mL/min/1.73 m 2 and 34.5%, respectively, improving to -1.2 mL/min/1.73 m 2 and 31.1% after maximal optimization of the formula to study data. The bias and precision of the ECOS were 0.7 mL/min/1.73 m 2 and 32.6%, respectively, improving to -0.1 mL/min/1.73 m 2 and 16.6% after maximal optimization of the System to study data. The prediction of GFR using ECOS was improved by accounting for the center from where clinical and laboratory measurements originated within the Connectionist model. Conclusion Algebraic formulas will be associated with greater prediction error in routine clinical practice than in the original trials, and machine intelligence is more likely to predict GFR accurately in this setting.
Stefan Schliebs - One of the best experts on this subject based on the ideXlab platform.
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2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
Neural Networks, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
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quantum inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling
International Joint Conference on Neural Network, 2009Co-Authors: Stefan Schliebs, Susan P Worner, Michael Defoin Platel, Nikola KasabovAbstract:The paper introduces a framework and implementation of an integrated Connectionist System, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a Naive Bayesian Classifier.
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integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
International Joint Conference on Neural Network, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
Susan P Worner - One of the best experts on this subject based on the ideXlab platform.
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2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
Neural Networks, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
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quantum inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling
International Joint Conference on Neural Network, 2009Co-Authors: Stefan Schliebs, Susan P Worner, Michael Defoin Platel, Nikola KasabovAbstract:The paper introduces a framework and implementation of an integrated Connectionist System, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a Naive Bayesian Classifier.
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integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
International Joint Conference on Neural Network, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
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comparison of a self organising map and simple evolving Connectionist System for predicting insect pest
2006Co-Authors: Michael J Watts, Susan P WornerAbstract:A comparison of two artificial neural network methods for predicting the risk of insect pest species establishment in regions where they are not normally found is presented. The ANN methods include a well-known unsupervised learning algorithm and a relatively new supervised constructive method. A New Zealand pest species assemblage as an example was used to compare model predictions. Both methods gave similar results for already established and non-established species. Keyword: Self-Organising Maps, Evolving Connectionist Systems, pest invasion prediction.
Michael Defoinplatel - One of the best experts on this subject based on the ideXlab platform.
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2009 special issue integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
Neural Networks, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
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integrated feature and parameter optimization for an evolving spiking neural network exploring heterogeneous probabilistic models
International Joint Conference on Neural Network, 2009Co-Authors: Stefan Schliebs, Michael Defoinplatel, Susan P Worner, Nikola KasabovAbstract:This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated Connectionist System, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.
Joao Luis Garcia Rosa - One of the best experts on this subject based on the ideXlab platform.
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a biologically inspired Connectionist System for natural language processing
Brazilian Symposium on Neural Networks, 2002Co-Authors: Joao Luis Garcia RosaAbstract:Nowadays artificial neural network models often lack many physiological properties of the nervous cell. Current learning algorithms are more oriented to computational performance than to biological credibility. The aim of this paper is to propose an artificial neural network System, called Bio-/spl theta/R, including architecture and algorithm, to take care of a natural language processing problem, the thematic relationship, in a biologically inspired Connectionist approach. Instead of feedforward or simple recurrent network, it is presented as a bi-directional architecture. Instead of the well-known biologically implausible backpropagation algorithm, a neurophysiologically motivated one is employed to account for linguistic thematic role assignment in natural language sentences. In addition, several features concerning biological plausibility are also included.
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A biologically motivated Connectionist System for predicting the next word in natural language sentences
IEEE International Conference on Systems Man and Cybernetics, 2002Co-Authors: Joao Luis Garcia RosaAbstract:Recent artificial neural network models lack many physiological properties of the neuron (Rocha 1992; Rosa 2001). Current learning algorithms are more oriented to computational performance than to biological credibility. The aim of this paper is to propose an artificial neural network System, called Bio-Pred, to take care of natural language processing word prediction, in a biologically inspired Connectionist approach. Instead of the well-known biologically implausible back-propagation algorithm (Crick 1989; Rumelhart et al., 1986), a neurophysiologically motivated one is employed (O'Reilly 1996) in a bi-directional Connectionist architecture to account for next word prediction in natural language sentences. In addition, several features concerning biological plausibility are also included, for instance, distributed representations. Comparisons are made between Bio-Pred and a System that uses the same word representation and the same next word prediction (Rosa 2002). The differences lie in the architecture employed-bi-directional architecture versus simple recurrent network (Elman 1990)-and in the learning algorithm-a neurophysiologically inspired procedure versus the biologically implausible back-propagation. The main contribution of Bio-Pred is to make an attempt to restore biological inspiration of current Connectionist Systems.