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In everchanging threat emitter environment, specific emitter identification (SEI) technology extracts subtle but persistent features from received pulse signal to create a fingerprint unique to a specific radar. Unlike conventional five parameters deinterleaving algorithm, which can be grossly ambiguous for radar emitter sorting, the SEI technology provides hardware specific identification. In this...
A novel support vector machine (SVM) with weighted features is proposed. To assign appropriate weights for each feature, a mutual information (MI) based approach is presented. Although the calculation of feature weights may add an extra computational cost, the proposed method generally exhibits better generalization performance over the traditional SVM. The numerical studies on one synthetic and five...
Traffic flow forecasting is an important issue for the application of intelligent transportation systems (ITS). How to improve the traffic flow forecasting precision is a crucial problem. Traffic models in different time sections have great differences. The forecasting precision could be improved if the traffic flow forecasting models were built on different time sections respectively. Traffic flow...
Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robotics aims to design computational systems that are endowed with such an intrinsic motivation mechanism. There are possible links between developmental robotics and machine learning. Affective computing takes into account emotions...
Entropy partition method for complex system has been applied in many ldnds of fields. In this paper, we improve the calculation of correlative measure for both discrete variables and continuous variables, and apply this method in vascular endothelial dysfunction (ED) discrete data and neuro-endocrine-immune (NEI) continuous data respectively. The partition results show this entropy partition methodpsilas...
Conventional cost functions of adaptive filtering are usually related to the errorpsilas dispersion, such as errorpsilas moments or errorpsilas entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of the error distribution. In this work, we propose a new notion of filtering (or estimation) in which the errorpsilas probability density function (PDF) is shaped into a desired one. As...
In this paper, we analyze the information feature of principal component analysis (PCA) deeply based on information entropy. According to idea of entropy function, a new weighted information functions (WIF) is proposed, and the information content of data matrix X is measured by it. Based on WIF, the information compression rate (ICR, RIC) and accumulated information compression rate (AICR, RIC) are...
Parameter estimation plays an important role in systems biology in helping to understand the complex behavior of signal transduction networks. The problem becomes more intense as the inherent stochasticity of the signaling mechanism involves noise components of non-Gaussian nature. A novel stochastic parameter estimation method has been developed where the aim is to obtain the optimal parameters corresponding...
It is well known that edge filters in the visual system can be generated by the InfoMax principle. But, such models are nonlinear and employ fully-connected network structures. In this paper, a new artificial network model is proposed, which is based on the ldquoInfoMinrdquo principle and linear multilayer ICA (LMICA). This network utilizes cumulant-based objective functions which are derived from...
AdaBoost is an algorithm with a procedure of selecting the data events from a dataset at each iteration sequence. The data events are selected stochastically using a random number generator. In this paper, a deterministic AdaBoost algorithm is proposed in contrast to the usual stochastic one. For doing this we derive the modified Fisherpsilas formulas moderated to the deterministic method. These formulas...
Approaches for solving a multiclass classification problem by support vector machines (SVMs) are typically to consider the problem as combination of two-class classification problems. Previous approaches have some limitations in classification accuracy and evaluation time. This paper proposes a novel method that employs information-based dichotomization for constructing a binary classification tree...
Each moving object contains particular unique signatures that can be used for pattern classification via object recognition and identification. Information extracted from the spatial object feature recognition can be provided by independent basis functions to represent actual physical attributes of the moving objects. Compared with principal component analysis, independent component analysis is a...
Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms...
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