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In this paper we propose a universal strategy for the automatic interpretation of sensor signals. We focus on acoustic signals. However, any time series may be used. We assume that changes in an object's state cause a typical and reproducible change in the characteristics of the acquired sensor signal. In such cases we can train pattern recognizers basing on Hidden-Markov-Models or support vector...
In MIMO-OFDM systems, by matching transmitter parameters such as modulation order and coding rate, link adaptation can increase the throughput significantly. However, creating a tractable mathematical mapping model from environmental variables to transmitter parameters that allows the latter to be optimized in any sense, presents serious challenges due to the large number of variables involved, as...
This paper presents a video shot boundary detection system based on support vector machine (SVM) classification method. A hardware fully-parallel digital support vector machine (SVM) classifier is used to detect the shot boundary in a continuous video stream. The throughput is increased by employing a pipelined architecture in the feature extraction stage. Hardware SVM can detect both cut and gradual...
Recently ensemble classification has attracted serious attention of machine learning community as a solution for improving classification accuracy. The effect of the strategies for generating the members, combining the predictions and the size of the ensemble on the accuracy of the ensemble are of utmost interest to the researchers. In this paper, we propose and empirically evaluate a novel method...
Techniques for information hiding and steganography are becoming increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages is also becoming considerably more difficult. In this paper, we describe a universal approach to steganalyse the least significant bit steganography method for detecting the presence of hidden messages embedded within...
The discrimination of parameter probability distribution type is the key to structure reliability analysis. A support vector machine (SVM) intelligent recognition model of probability distribution law is presented aiming at traditional method disadvantage. The intelligent recognition model of probability distribution is constructed by SVM algorithm realization, network design and feature extraction,...
Preventive maintenance plays a very important role in the modern Heating, Ventilation and Air Conditioning (HVAC) systems for guaranteeing the thermal comfort, energy saving and reliability. The fault diagnosis on HVAC system is a difficult problem due to the complex structure of the HVAC and the presence of multi-excite sources. As the HVAC system fault information has inaccurate and uncertainty...
With the rapid development of the communication technology, the communication environment becomes more and more complicated these years. Many signal modulation types are used simultaneously in digital TV communication systems. Therefore, a need arises for modulation classification that can automatically detect the incoming modulation type. In this paper, we propose a new approach for modulation classification,...
Because logistics system was an uncertain, nonlinear, dynamic and complicated system, it was difficult to describe it by traditional methods. The support vector machine (SVM) has the ability of strong nonlinear function approach, it has the ability of strong generalization and it also has the feature of global optimization. In this paper, a modeling and forecasting method of urban logistics demand...
Enterprise performance evaluation is an important means of enterprise management, which can diagnose the whole development status of enterprise. Data envelopment analysis (DEA) is one of the most frequently used evaluation methods and support vector machine (SVM) is a novel method of data mining, which can be used for prediction and regression. Based on DEA and SVM, the paper proposes a method for...
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...
Support vector machine is a new machine learning technique developed on the basis of statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model...
A new steganalysis method is proposed to detect the exists of hidden information using character substitution in texts. This is done by utilizing Support Vector Machine (SVM) as a classifier to classify the characteristic vector input into SVM. The most important step of this detection algorithm is the construction of a proper characteristic vector. Under the prerequisite that the secret bits to be...
Support Vector Machine has the convenient superiority in the classification. Recently it has been extended to the domain of regression problems. However, due to the increasing index, excess input data and complicated system structure, it is difficult to achieve good accuracy in results. This paper adopts combination method of rough set and support vector machine so as to establish rough set attribute...
With the rapid development of the Internet, the P2P (Peer-to-Peer) technology which is characterized by no utilization of any servers with centralized functions has kept advancing apace. However, how to improve the accuracy of the P2P traffic identification efficiently is still a challenging problem. In this paper, we propose a new approach for P2P traffic identification, which uses a novel Support...
This paper deals with the study of a water quality prediction model through application of LS-SVM in Liuxi River in Guangzhou. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome...
With the rapid development of real estate, the risk of investment is also increasing rapidly. So the risk of predicting and controlling the real estate investment has become the key to the success or failure of the project. In this paper, a support vector machine (SVM) modeling approach for real estate investment risk prediction is proposed at first, which is made use of its merits of structural risk...
The feature subset selection is a key preprocessing part in the detection of the stored-grain insects based on the image recognition technology. According to the global optimization ability of the particle swarm optimization (PSO) and the superior classification performance of the support vector machines (SVM), this study proposed a method based on PSO and SVM to improve the classification accuracy...
Sentiment classification is an applied technology with great significance. It can help people find right reviews in a more efficient way. In this paper, we present a novel efficient method for BBS sentiment classification. Through extracting sentiment-bearing words from WordNet using the maximum entropy, a ranking criterion based on a function of the probability of having Polarity or not is introduced...
Ship pitching influences mostly ship motion, it's important to study ship pitching modeling and prediction in order to improve ship's seaworthiness. Based on the random character of ship movement, this paper put forward a method for prediction of ship pitching movement with SVM. Based on the phase-space reconstruction theory, the method, the characteristic, and the selecting of the key parameters...
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