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Neural network ensemble is a learning paradigm where many neural networks are used together to solve a particular problem. This paper presents a new method to construct a neural network ensemble (NNE) based on Correlation, Interaction Validation and Entropy (CIENNE). The method consists of two parts: a sub-algorithm to construct best component neural networks with Correlation and Interaction Validation,...
The concept of ensemble feature selection has been raised by Optiz in his earlier work. And yet, for models like neural networks, new models should be trained and created for every change in its feature subspace, this problem may become tricky when evolutionary algorithms are used to select features, for the slow-training process of neural networks may dramatically extend the whole process of ensemble...
It is worthwhile to point out the fact that nature of given data plays considerable role in classifying the data accurately. To select an appropriate classifier for certain type of data, we are required to understand the behavior of classifiers on different data characteristics. The varying dimensions, number of instances, class labels, data correlation, and data distribution on different data classes,...
Extension neural network is a new method based on Extenics and neural networks, it is full use of the Extension of qualitative and quantitative description of the advantages, but also consider the parallel structure characteristics, of neural network. This article describes the extension theory and neural network fusion extension neural network structure and introduce ENN algorithm based on genetic...
Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named "Semi CCA" that allows us to incorporate additional unpaired samples for mitigating overfitting...
In this paper, we propose an artificial neural network approach to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARI). In order to accurately describe the structural properties of ARIs, besides the popularly used 2-dimensional (2D) descriptors, we have used 3-dimensional (3D) molecular descriptors which are obtained through the DRAGON software...
There are no algorithms that generally perform better or worse than random when looking at all possible data sets according to the no-free-lunch theorem. A specific forecasting method will hence naturally have different performances in different empirical studies. This makes it impossible to draw general conclusions, however, there will of course be specific problems for which one algorithm performs...
A evolutionary programming is proposed in this paper to automatically design neural networks(NNS) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to find the best collaboration connection during the evolutionary process...
It is well-known that ensemble performance relies heavily on sufficient diversity among the base classifiers. With this in mind, the strategy used to balance diversity and base classifier accuracy must be considered a key component of any ensemble algorithm. This study evaluates the predictive performance of neural network ensembles, specifically comparing straightforward techniques to more sophisticated...
Transfer learning is a new learning paradigm, in which, besides the training data for the targeted learning task, data that are related to the task (often under a different distribution) are also employed to help train a better learner. For example, out-dated data can be used as such related data. In this paper, we propose a new transfer learning framework for training neural network (NN) ensembles...
Machine learning algorithms were used for feature selection and model generation of customized docking score functions for known inhibitors of Mycobacterium tuberculosis enoyl acyl carrier protein reductase. The features included small molecule descriptors derived from MOE, Accord, and Molegro as well as in silico docking energies/scores from GOLD and Autodock. The resulting models can be used to...
A specific issue in the voice password system is addressed in this paper: When the text content of target speaker's enrollment password has been already known by imposters, they can do a well-behaved impersonation using the same text content as the target speaker. This results in a much higher false acceptance than the traditional voice password system. N-gram based nearest neighbor algorithm is proposed...
This paper presented a new prediction model for Pressure-Volume-Temperature (PVT) properties based on the recently introduced learning algorithm called Sensitivity Based Linear Learning Method (SBLLM) for two-layer feedforward neural networks. PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties such as bubble-point pressure and...
Character design artists typically use shape, pose and proportion as the first design layer to express role, physicality and personality traits. Inspired by this we approach the problem of automatic character synthesis by attempting to learn relations among the body-shape, proportions, pose, and trait labels from finished art. In our prior work, we have designed an online game framework to collect...
In view of the defect that the gray method can only predict the tendency approximately and artificial neural network can not predict the future tendency really, a new organic gray neural network model was proposed by the advantages of ARMA and rbf neural network. The neural network was trained to get the optimal structure of neural network. According to the dynamic law of one river water quality in...
Calculation of reserves in an oil reservoir and the determination of its performance and economics require good knowledge of its physical properties. Accurate determination of the pressure-volume-temperature (PVT) properties such as the bubble point pressure (Pb) and the oil formation volume factor (Bob) are important in the primary and subsequent development of an oil field. This paper proposes Support...
The problem of outlier detection is well studied in the fields of Machine Learning (ML) and Knowledge Discovery in Databases (KDD). Both fields have their own methods and evaluation procedures. In ML, Support Vector Machines and Parzen Windows are well-known methods that can be used for outlier detection. In KDD, the heuristic local-density estimation methods LOF and LOCI are generally considered...
This paper represents a currency recognition system using ensemble neural network (ENN). The individual neural networks (NN) in an ENN are trained via negative correlation learning. The object of using negative correlation learning (NCL) is to expertise the individuals in an ensemble on different parts or portion of input patterns. The available currencies in the market consist of new, old and noisy...
Motivation: Machine learning in bioinformatic sheds light on the traditional biography research. Through the prediction of functional genes from amino sequence information, the experimental cost for new gene finding could be reduced. Results: We propose an effective machine-learning approach based on artificial neural networks (ANN), to assess the chance of a protein in rice to be disease resistant...
The same stimulus can evoke different emotions for different individuals. Incorporating personalized construal of stimuli is how appraisal models differ from dimensional models of emotion. Scherer formulated a model of the cognitive antecedents of emotions and analyzed recollections of events and emotions that his participants provided. In the present study, we were interested whether Scherer's appraisal...
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