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Search Results for Collection: KEDRI - the Knowledge Engineering and Discovery Research Institute
Now showing items 1-10 of 10
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(IEEE, AUT University; 2004)This paper introduces a novel connectionist approach to neural network modelling that integrates dynamic gene networks within neurons with a neural network model. Interaction of genes in neurons affects the dynamics of the ... -
(IEEE, AUT University; 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, ... -
(IEEE, AUT University; 2004)This paper introduces an approach of Weighted Data Normalization (WDN) for Radial Basis Function (RBF) type of neural networks. It presents also applications for medical decision support systems. The WDN method optimizes ... -
A two-stage methodology for gene regulatory network extraction from time-course gene expression data(IEEE, AUT University; 2004)The discovery of gene regulatory networks (GRN) from time-course gene expression data (gene trajectory data) is useful for (1) identifying important genes in relation to a disease or a biological function; (2) gaining an ... -
(IEEE, AUT University; 2004) -
(IEEE, AUT University; 2004)This paper introduces a novel transductive support vector machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in bioinformatics - promoter recognition. While inductive reasoning is ... -
(IEEE, AUT University; 2004)The paper introduces a combination of adaptive neural network systems and statistical method for integrating speech and face image information for person identification. The method allows for the development of models of ... -
(IEEE, AUT University; 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 ... -
(IEEE, AUT University; 2004)The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other ... -
(IEEE, AUT University; 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 ...
Now showing items 1-10 of 10