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Feature selection techniques have become an apparent need in many bioinformatics applications. In this article, taking into account the protein-protein interactions in sequence and spatial structure of protein, we propose a new correlation based method to capture these correlations between features to check feature redundancy, and implement a corresponding feature selection algorithm CBPFS. Features...
The paper deals with a method for accurate semisymbolic time-domain analysis of highly idealized linear lumped circuits. Pulse and step responses can be computed by means of the partial fraction decomposition. The procedure relies on an accurate computation of poles of the transfer function. The well known problem of the QR and QZ algorithms is their poor accuracy in the case of multiple roots. Moreover,...
We present a new algorithm for learning a convex set in n-dimensional space given labeled examples drawn from any Gaussian distribution. The complexity of the algorithm is bounded by a fixed polynomial in n times a function of k and ϵ where k is the dimension of the normal subspace (the span of normal vectors to supporting hyperplanes of the convex set) and the output is a hypothesis that correctly...
Centrality measures are crucial in quantifying the roles and positions of vertices in networks. An important measure is betweenness, which is based on the number of shortest paths that vertices fall on. However, betweenness is computationally expensive to derive, resulting in much research on efficient techniques. We note that in many applications, the key interest is on the high-betweenness vertices...
The homologous software detection technology plays a very important role in the work of intellectual property protection by identifying code plagiarism. Plagiarism mainly happens as copy-and-paste of the code, replacing the name of functions or variables, reordering the sequence of the statement, type redefinition, and so on. At present, there are three homologous software detection technology methods...
In recent years, Internet traffic classification using machine learning has become a new direction in network measurement. Because supervised clustering algorithm need accuracy of training sets and it can not classify unknown application, we introduced complex network's community detecting algorithm, a new unsupervised classify algorithm, which have previously not been used for network traffic classification...
Based on the edge feature extraction of oracle bones, two-dimensional fragments contour matching algorithm based on angular sequence is proposed to obtain the most likely njugating outcome between two pieces. The algorithm is combined with the multi-scale space, which not only solves the sharp corner problem of oracle bone conjugating effectively, also improves the computational efficiency and accuracy...
Credit scoring has become a very important task in the credit industry and its use has increased at a phenomenal speed through the mass issue of credit cards since the 1960s. Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have...
In this paper, we propose a hybrid approach to the wireless sensor network (WSN) localization problem. The proposed approach harnesses the strengths of two techniques: RF mapping and cooperative ranging, to overcome the potential weaknesses in one another. The idea is to first allow every node to obtain an initial estimate of its own position in a neighbor-independent way using a coarse-grained RF...
Document clustering is an important tool for applications such as search engines and document browsers. It enables the user to have a good overall view of the information contained in the documents. The well-known methods of document clustering, however, do not really address the special problems of text document clustering: very high dimensionality of the document, very large size of the datasets...
A new method based on kernel which can measure class separability in feature space is proposed in this paper for existing error accumulation when the hierarchical SVMs is used to diagnose multiclass network fault. This method has defined metrics of sample distribution in feature space, which are used as the rule of constructing hierarchical SVMs. Experiment results show that this method can restrain...
A novel dynamic particle swarm optimization algorithm based on chaotic mutation (DCPSO) is proposed to solve the problem of the premature and low precision of the common PSO. Combined with linear decreasing inertia weight, a kind of convergence factor is proposed based on the variance of the populationpsilas fitness in order to adjust ability of the local search and global search; The chaotic mutation...
The naive Bayesian classifier is widely used because of itpsilas simplicity and effectiveness. But it has a strict assumption about the independence for each attribute, which is not obviously hold in real world domains. Many efforts have been made to relax the independence and improve the performance of the naive Bayesian classifier. Tree Augmented Naive Bayes (TAN) classifier was proved to be one...
The basic K-means is sensitive to the initial centre and easy to get stuck at local optimal value. To solve such problems, a new clustering algorithm is proposed based on simulated annealing. The algorithm views the clustering as optimization problem, the bisecting K-means splits the dataset into k clusters at first, and then run simulated annealing algorithm using the sum of distances between each...
Associative classification has high classification accuracy and strong flexibility. However, it still suffers from overfitting since the classification rules satisfied both minimum support and minimum confidence are returned as strong association rules back to the classifier. In this paper, we propose a new association classification method based on compactness of rules, it extends Apriori Algorithm...
An optimized method to design and implement digital three-phase phase-locked loop (PLL) based on FPGA is presented in this paper. The PLL fits in electric power system as well as other fields. At first, principle and basic structure of the PLL including phase discriminator, loop filter and voltage controlled oscillator (VCO) are introduced, then these modules are designed in VHDL language with blocking...
Image registration is an important task in the field of computer vision and pattern recognition. And the applied values is also reflected in the study of remote sensing, medical imaging and the object indentifying of multi-sensor fusion. In this paper, a new subpixel registration methods which based on wavelet analysis was proposed by improving polynomial subdivision algorithm and pixel level registration...
Transductive confidence machines (TCMs) when used in classification problems can provide us with reliability for every classification. Many machine learning algorithms, such as KNN algorithm, etc., have been incorporated with TCM, while there's no SOM classification method based on TCM. Considering properties of SOM map unit, this paper first designs a novel nonconformity measurement and TCM-SOM classification...
In this paper, we introduce new bounds on the system throughput and response time of closed, single-class BCMP queueing networks with load-dependent stations. Under the assumption that stations relative service rates are non-decreasing functions of their queue lengths, the bounds derive from the monotonicity of system throughput and queue-lengths and exploit the asymptotic equivalence that exists...
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