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Image classification mainly uses the classifier to classify the extracted image features. In the traditional image feature extraction, it is difficult to set the appropriate feature patterns for the complex images. Simultaneously, the training algorithm of the classifier also affects the accuracy of image classification. In order to solve these problems, the combination of deep belief networks and...
With the rapid development of clustering analysis technology, there have been many application-specific clustering algorithms, such as text clustering. K-Means algorithm, as one of the classic algorithms of clustering algorithms, and a textual document clustering algorithms commonly used in the analysis process, is widely used because of its simple and low complexity. This article in view of two big...
Spectrum sensing is a critical prerequisite in envisioned applications of wireless cognitive sensor networks which promise to resolve the perceived bandwidth scarcity versus under-utilization dilemma. The Kernel method is a very powerful tool in machine learning. The trick of kernel has been effectively and extensively applied in many areas of machine learning. In this paper, we propose a novel spectrum...
Aiming to the problem of weak primary user signal detection rate in low signal-to-noise ratio environments, we propose a novel spectrum sensing method based on the principal component analysis (PCA) and random forest (RF). From the received radio signal, a set of cyclic spectrum features are first calculated, and the PCA is applied to extract the most discriminate feature vector for classification...
This study aims to explore the case of robust speaker recognition with multi-session enrollments and noise, with an emphasis on optimal organization and utilization of speaker information presented in the enrollment and development data. This study has two core objectives. First, we investigate more robust back-ends to address noisy multi-session enrollment data for speaker recognition. This task...
Distribution of data stream is always changed in the real world. This problem is usually defined as concept drift [1]. The state-of-the-art decision tree classification method CVFDT[2] can solve the concept drift problem well, but the efficiency is debased because of its general method of handling instances in CVFDT without considering the types of concept drift. In this paper, an algorithm called...
This study explores various back-end classifiers for robust speaker recognition in multi-session enrollment, with emphasis on optimal utilization and organization of speaker information present in the development data. Our objective is to construct a highly discriminative back-end framework by fusing several back-ends on an i-vector system framework. It is demonstrated that, by using different information/data...
Mining of association rules has become an important area in the research on data mining. However the traditional approaches based on support-confidence framework maybe generate a great number of redundant and wrong association rules. In order to solve the problems, a correlation measure is defined and added to the mining algorithm for association rules. According to the value of correlation measure,...
As an important method for the evaluation of quantity of area rainfall, Thiessen polygon method is widely applied because of its high calculation accuracy and fast computation. The calculation of Thiessen polygon method is simple because only area data of sample point is needed. When dataset is stable, accuracy of Thiessen polygon method is higher than the arithmetic average method. But there are...
EM (expectation-maximization) algorithm is a classical method for parameter estimation of HMM (Hidden Markov model ). Concerning that EM algorithm is easily affected by initial parameter values, we proposed a mixture splitting algorithm based on decision boundary confusion (DBC) to describe more about boundary distribution. The algorithm mainly includes three aspects: firstly the number of incremented...
All-to-all personalized communication, also known as complete exchange, is one of the most dense communication patterns in parallel computing. In this paper, we propose new indirect algorithms for complete exchange on all-port ring and tori. The new algorithms fully utilize all communication links and transmit messages along shortest paths to completely achieve the theoretical lower bounds on message...
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