Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title
Now showing items 21-40 of 54
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Evolving intelligent systems: methods, learning, & applications
(IEEE, 2006)The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized in this short communication. ... -
Evolving, Dynamic Clustering of Spatio/Spectro-Temporal Data in 3D Spiking Neural Network Models and a Case Study on EEG Data
(Springer, 2017)Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little ... -
Exploring associations between changes in ambient temperature and stroke occurrence: comparative analysis using global and personalised modelling methods
(Springer, 2011)Stroke is a major cause of disability and mortality in most economically developed countries that increasing global importance. Up till now, there is uncertainty regarding the effect of weather conditions on stoke occurrence. ... -
Fast Neural Network Ensemble Learning via Negative-Correlation Data Correction
(IEEE, 2005)This letter proposes a new negative correlation (NC) learning method that is both easy to implement and has the advantages that: 1) it requires much lesser communication overhead than the standard NC method and 2) it is ... -
Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method
(IEEE, 2004)Clustering time course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network (GRN) modeling and discovery as it significantly reduces the dimensionality ... -
A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification
(Elsevier Inc., 2016)k nearest neighbors (kNN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, ... -
Incremental learning for online face recognition
(IEEE, 2005)In this paper, a new approach to face recognition is presented in which not only a classifier but also a feature space of input variables is learned incrementally to adapt to incoming training samples. A benefit of this ... -
Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition
(IEEE, 2005)The paper presents an integrated approach to incremental learning in autonomous systems, that includes both pattern recognition and feature selection. The approach utilizes evolving connectionist systems (ECoS) and is ... -
Incremental Linear Discriminant analysis for classification of Data Streams
(IEEE, 2005)This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form ... -
Inductive vs transductive inference, global vs local models: SVM, TSVM, and SVMT for gene expression classification problems
(IEEE, 2004)This paper compares inductive-, versus transductive modeling, and also global-, versus local models with the use of SVM for gene expression classification problems. SVM are used in their three variants - inductive SVM, ... -
Integrated feature and parameter optimization for an evolving spiking neural network
(Springer, 2009)This study extends the recently proposed Evolving Spiking Neural Network (ESNN) architecture by combining it with an optimization algorithm, namely the Versatile Quantum-inspired Evolutionary Algorithm (vQEA). Following ... -
Integrated Gene Expression analysis of Multiple Microarray data sets based on a Normalization Technique and on Adaptive Connectionist model
(IEEE, 2003)Research with microarray gene expression analysis has primarily been on expression profiling based on one set of microarray data. This paper presents a novel approach to integrated analysis and modeling of microarray data ... -
Longitudinal Study of Alzheimer’s Disease Degeneration through EEG Data Analysis with aNeuCube Spiking Neural Network Model
(IEEE, 2016)Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroencephalography (EEG) data collected from people affected by Alzheimer’s Disease (AD) and people diagnosed with mild ... -
Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modeling, and understanding of stream data
(IEEE, 2016)This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network (SNN) architecture. This optimized mapping extends ... -
Method for training a spiking neuron to associate input-output spike trains
(AUT University, 2011)We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple ... -
Mobile robot navigation - some issues in controller design and implementation
(IEEE Instrumentation and Measurement, Malaysia (IM), 2009) -
Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture
(Macmillan Publishers Limited, 2018)Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network ... -
Modelling the effect of genes on the dynamics of probabilistic spiking neural networks for computational neurogenetic modelling
(AUT University, 2011)Computational neuro-genetic models (CNGM) combine two dynamic models – a gene regulatory network (GRN) model at a lower level, and a spiking neural network (SNN) model at a higher level to model the dynamic interaction ... -
Network-based method for inferring cancer progression at the pathway level from cross-sectional mutation data
(IEEE, 2016)Large-scale cancer genomics projects are providing a wealth of somatic mutation data from a large number of cancer patients. However, it is difficult to obtain several samples with a temporal order from one patient in ... -
Neural Systems for solving the inverse problem of recovering the Primary Signal Waveform in potential transformers
(IEEE, 2003)The inverse problem of recovering the potential transformer primary signal waveform using secondary signal waveform and information about the secondary load is solved here via two inverse neural network models. The first ...