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Performance of the support vector machine strongly depends on parameters settings. One of the most common algorithms for parameter tuning is grid search, combined with cross validation. This algorithm is often time consuming and inaccurate. In this paper we propose the use of stochastic metaheuristic algorithm, firefly algorithm, for effective support vector machine parameter tuning. The experimental...
The availability of chemical libraries with millions of compounds makes the process of identifying lead compounds very hard. The identification of these compounds is the backbone step of drug discovery process. Hierarchical clustering algorithms are used for that purpose. One of the most popular hierarchical clustering algorithms that are used in many applications in the drug discovery process is...
It has been demonstrated that the multi-channel SEMG allows assessment of anatomical and physiological individual motor unit characteristics. The motor unit action potential(MUAP) can be decomposed from SEMG to obtain these properties. This paper presented a method to exact MUAP from multi-channel SEMG. The firing instants of each motor unit(MU) were separated by K-means clustering Convolution Kernel...
The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms...
Data processing is usually based on uniformly sampled data in time. This sampling scheme is often unnecessary for non-stationary signals because samples are also taken in inactive regions. In case of embedded system, this useless samples significantly increase the power consumption. One solution to avoid this useless power consumption is the level crossing sampling scheme (LCSS). This method is not...
The mean-shift algorithm is a popular approach for clustering and mode location estimation, notably for medical image processing. However, it comes with a high computational cost. In this work, we investigate the possibility of reducing this load using a sparse approximation of the kernel density estimator, based on an incoherence criterion. We derive a sparse scalable mean-shift algorithm that includes...
Similarity measurement for spectral clustering has been well-studied in recent years due to its crucial role on describing the intrinsic structure of data points. In this paper, we propose a hybrid attributes similarity measure method to process the Gaussian kernel affinity matrix. Compared with traditional global or local scale methods, our new similarity measurement has a rather robustness to reflect...
Relationships between entities in datasets are often of multiple types, which can naturally be modeled by a multi-layer graph; a common vertex set represents the entities and the edges on different layers capture different types of relationships between the entities. In this paper, we address the problem of analyzing multi-layer graphs and propose methods for clustering the vertices by efficiently...
We develop a new non-parametric hierarchical information theoretic clustering algorithm based on implicit estimation of cluster densities using k-nearest neighbors (k-nn). Compared to a kernel-based procedure, our k-nn approach is very robust with respect to the parameter choices, with a key ability to detect clusters of vastly different scales. Of particular importance is the use of two different...
This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation, adaptation, mergence, split and elimination. Experiments...
Time series prediction methods applied to chaotic signals affected by noise use a continuous pattern function as the least-squares estimate of an unknown deterministic map. The noise variance around the continuous pattern function is not always constant but may exhibit spatial variability, which directly affects prediction performance. In this paper we propose a novel approach for increasing predictor...
In the image analysis, image segmentation is the operation that divides image into set of different segments. The paper deals about common color image segmentation techniques and methods. The advantages and disadvantage of each one will be described in this paper. At the end of the paper, the evaluation criterion will be introduced and applied on the algorithms results. Five most used image segmentation...
To solve the problem of initiating tracks for multi-target in dense clutters environment, a Mean shift track initiation algorithm based on Hough transform is proposed. In the algorithm, firstly, hough transform is applied to transform observation points from input space, referred to as feature space into curves in a special parameter space; then a Mean shift clustering algorithm is executed to cluster...
The new method stated in this paper is to model the multiple objects in the visual sequence into two-dimensional multi-peak probability distribution, which raised a new multiple-objects tracking method with particle filter. The results of importance resampling by the particle filter represent the probability distributions of the objects. Firstly, it gains the probability distribution model points...
As radar signal environments become denser and more complex, the capability of high-speed and accurate signal analysis is required for ES (electronic warfare support) system to identify individual radar signals at real-time. In this paper, we propose the novel clustering algorithm of radar pulses to alleviate the load of signal analysis process and support reliable analysis. The proposed algorithm...
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