Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title
Now showing items 17-36 of 56
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Efficient global clustering using the greedy elimination method
(IEEE, 2004)A novel global clustering method called the greedy elimination method is presented. Experiments show that the proposed method scores significantly lower clustering errors than the standard K-means over two benchmark and ... -
Evolutionary Computation for Dynamic Parameter Optimisation of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics
(IEEE, 2003)The paper describes a method of using evolutionary computation technique for parameter optimisation of evolving connectionist systems (ECOS) that operate in an online, life-long learning mode. ECOS evolve their structure ... -
Evolving connectionist systems based role allocation of robots for soccer playing
(IEEE, 2005)For a group of robots (multi-agents) to complete a task, it is important for each of them to play a certain role changing with the environment of the task. One typical example is robotic soccer in which a team of mobile ... -
Evolving Connectionist Systems for Adaptive Learning and Knowledge Discovery: Trends and Directions
(Elsevier, 2015)This paper follows the 25 years of development of methods and systems for knowledge-based neural network systems and more specifically the recent evolving connectionist systems (ECOS). ECOS combine the adaptive/evolving ... -
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 ... -
Machine Learning Methods for the Study of Cybersickness: A Systematic Review
(Springer, 2022)This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly ... -
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 ...