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This paper discusses the framework for virtual collaborative environment for researchers, practitioners, users and learners in the areas of computational intelligence and machine learning (CIML) that is currently developed by our group. It also outlines main features of the community portal under construction that will support communication and sharing of computational resources. In particular, selected...
The study and inference of biological pathways and gene regulation mechanisms has become a vital component of modern medicine and drug discovery. Gene expression studies make it possible to understand these mechanisms by simultaneously measuring the expression level of thousands of genes. These data though rich in information are also prone to many quality control issues that ultimately result in...
Exploiting additional information to improve traditional inductive learning is an active research in machine learning. When data are naturally separated into groups, SVM+[7] can effectively utilize this structure information to improve generalization. Alternatively, we can view learning based on data from each group as an individual task, but all these tasks are somehow related; so the same problem...
Generative topographic mapping (GTM) is a manifold learning model for the simultaneous visualization and clustering of multivariate data. It was originally formulated as a constrained mixture of distributions, for which the adaptive parameters were determined by maximum likelihood (ML), using the expectation-maximization (EM) algorithm. In this formulation, GTM is prone to data overfitting unless...
In this work we use support vector machine to predict polyadenylation sites (Poly (A) sites) in human DNA and mRNA sequences by analyzing features around them. Two models are created. The first model identifies the possible location of the Poly (A) site effectively. The second model distinguishes between true and false Poly (A) sites, hence effectively detect the region where Poly (A) sites and transcription...
The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the...
Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability...
Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent...
In this paper, an independent component analysis (ICA) based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the...
This paper presents an on-line, continuously learning mechanism for sequence data. The proposed approach is based on SOINN-DTW method (Okada and Hasegawa, 2007), which is designed for learning of sequence data. It is based on self-organizing incremental neural network (SOINN) and dynamic time warping (DTW). Using SOINNpsilas function represents the topological structure of online input data, the output...
This paper presents a novel method of rule extraction by encoding the knowledge of the data into an SVM classification tree (SVMT), and decoding the trained SVMT into a set of linguistic association rules. The method of rule extraction over the SVMT (r-SVMT), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the obtained decision-tree structure...
The ensemble paradigm for machine learning has been studied for more than two decades and many methods, techniques and algorithms have been developed, and increasingly used in various applications. Nevertheless, there are still some fundamental issues remaining to be addressed, and an important one is what factors affect the accuracy of an ensemble, and to what extent they do, which is thus taken...
The purpose of this study was to develop a product design model for impact toughness estimation of low-alloy steel plates. Based on these estimates, the rejection probability of steel plates can be approximated. The target variable was formulated from three Charpy-V measurements with a LIB transformation, because the mean of the measurements would have lost valuable information.The method is suitable...
Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a non-traditional way. The main idea behind success of NMF is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more...
Model field theory (MFT) is a powerful tool of pattern recognition, which has been used successfully for various tasks involving noisy data and high level of clutter. Detection of spatio-temporal activity patterns in EEG experiments is a very challenging task and it is well-suited for MFT implementation. Previous work on applying MFT for EEG analysis used Gaussian assumption on the mixture components...
Pattern classification is an important task in speech recognition and speaker verification. Given the feature vectors of an input the goal is to capture the characteristics of these features unique to each class. This paper deals with exploring Auto Associative Neural Network (AANN) models for the task of speaker verification and speech recognition. We show that AANN models produce comparable performance...
Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks feedback linearization component. Thus, RODES automatically discovers the...
Generative topographic mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal with noni. i.d. data such as multivariate time series in a variant called GTM through time (GTM-TT), defined as a constrained hidden Markov model (HMM). In this paper, we provide...
The research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method for general Finite Mixture Models was recently proposed and subsequently extended to generative topographic mapping (GTM), a manifold learning constrained mixture model that provides...
In order to implement a model-based predictive control methodology for a research greenhouse several predictive models are required. This paper presents the modelling framework and results about the models that were identified. RBF neural networks are used as non-linear auto-regressive and non-linear auto-regressive with exogenous inputs models. The networks parameters are determined using the Levenberg-Marquardt...
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