support vector machine

摘要： Nonlinearity impairments and distortions have been bothering the bandwidth constrained passive optical network(PON)system for a long time and limiting the develop-ment of capacity in the PON system.Unlike other works concentrating on the exploration of the complex equalization algorithm,we investigate the potential of constellation shap-ing joint support vector machine(SVM)classification scheme.At the transmitter side,the 8 quadrature amplitude modulation(8QAM)constellation is shaped into three designs to mitigate the influence of noise and distortions in the PON channel.On the receiver side,simple multi-class linear SVM classifiers are utilized to replace complex equalization methods.Simulation results show that with the bandwidth of 25 GHz and overall bitrate of 50 Gbit/s,at 10 dBm input optical power of a 20 km standard single mode fiber(SSMF),and under a hard-decision forward error correction(FEC)threshold,transmission can be realized by employing Circular(4,4)shaped 8QAM joint SVM classifier at the maximal power budget of 37.5 dB.

摘要： BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

摘要： Thermodynamic calculation is the theoretical basis for the study of initiation and detonation,as well as the prerequisite for forecasting the detonation performance of unknown explosives.Based on the VLWR(Virial-Wu)thermodynamic code,this paper introduced the universal solid equation of state(EOS)VINET.In order to truly reflect the compressibility of nanocarbon under the extremely high-temperature and high-pressure environment in detonation,an SVM(support vector machine)was utilized to optimize the input parameters of carbon.The detonation performance of several explosives with different densities was calculated by the optimized universal EOS,and the results show that the thermodynamic code coupled with the universal solid EOS VINET can predict the detonation performance parameters of explosives well.To investigate the application of the thermodynamic code with the improved VINET EOS in the working capacity of explosives,the interrelationship between pressure P-particle velocity u and pressure P-volume V were computed for the detonation products of TNT and HMX-based PBX(HMX:binder:insensitive agent=95:4.3:0.7)in the CJ isentropic state.A universal curve proposed by Cooper was used to compared the computed isentropic state,where the ratio of pressure to CJ state were plotted against the ratio of velocity to CJ state.The parameters of the JWL(Jones-Wilkins-Lee)EOS for detonation products were obtained by fitting the P-V curve.The cylinder tests of TNT and HMX-based PBX were numerically simulated using the LS-DYNA,it is verified that,within a certain range,the improved algorithm has superiority in describing the working capacity of explosives.

摘要： This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers.A comparison relationship between the excess misclassification error and the excess generalization error is provided;from this,along with the convex analysis theory,a kind of learning rate is derived.The results show that the performance of the classifier is effected by the outliers,and the extent of impact can be controlled by choosing the homotopy parameters properly.

摘要： Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

摘要： In social networks,many complex factors affect the prediction of user forwarding behavior.This paper proposes an improved SVM prediction method for user forwarding behavior of hot topics to improve prediction accuracy.Firstly,we consider that the improved Cuckoo Search algorithm can select the optimal penalty parameters and kernel function parameters to optimize the SVM and thus predict the user's forwarding behavior.Secondly,this paper considers the factors that affect the user forwarding behavior comprehensively from the user's own factors and external factors.Finally,based on the characteristics of the user's forwarding behavior changing over time,the time-slicing method is used to predict the trend of hot topics.Experiments show that the method can accurately predict the user's forwarding behavior and can sense the trend of hot topics.

摘要： Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer’s disease(AD)or amnestic mild cognitive impairment(aMCI).However,most studies examined traditional resting state functional connections,ignoring the instantaneous connection mode of the whole brain.In this case-control study,we used a new method called dynamic functional connectivity(DFC)to look for abnormalities in patients with AD and aMCI.We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant,and then used a support vector machine to classify AD patients and normal controls.Finally,we highlighted brain regions and brain networks that made the largest contributions to the classification.We found differences in dynamic function connectivity strength in the left precuneus,default mode network,and dorsal attention network among normal controls,aMCI patients,and AD patients.These abnormalities are potential imaging markers for the early diagnosis of AD.

摘要： Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of the biomarkers that enable the detection of Parkinson’s disease affection. Numerous researches are based on recordings from controlled environments;nonetheless fewer apply real circumstances. In the present study, three objectives were examined: recording fragmentation (paragraph, sentences, time-based), variable encodings (Pulse-Code Modulation [PCM], GSM-Full Rate [FR], G.723.1) and majority voting on 8 kHz records using multiple classifiers. Support Vector Machine (SVM), Long Short-Term Memory (LSTM), i-vector and x-vector classifiers were evaluated in contrast with SVM as baseline. The highest results in accuracy and F1-score were achieved using i-vector models. Although variable encodings generally caused decrease in Parkinson-disease recognition, decline was within 2% - 3% at best. Moreover, fragmentation did not yield a clear outcome though some classifiers performed with the very similar efficiency along the differently fragmented sets. Majority voting did produce a slight increase in classification performance compared to as if no aggregation is used.

摘要： Principal component analysis and generalized low rank approximation of matrices are two different dimensionality reduction methods. Two different dimensionality reduction algorithms are applied to the L1-CSVM model based on augmented Lagrange method to explore the variation of running time and accuracy of the model in dimensionality reduction space. The results show that the improved algorithm can greatly reduce the running time and improve the accuracy of the algorithm.

摘要： Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

摘要： To reduce the influences of outliers on support vector machine (SVM) classification problem, weconstructed a new tangent loss function. Since the tangent loss function is not smooth in some interval, asmoothing function was used to approximate it in this interval. According to this loss function, we got thecorresponding tangent SVM. The experimental results show that tangent SVM is less sensitive to outliersthan conventional SVM. So the proposed new loss function and tangent SVM are both effective.

摘要： To reduce the influences of outliers on support vector machine (SVM) classification problem, weconstructed a new tangent loss function. Since the tangent loss function is not smooth in some interval, asmoothing function was used to approximate it in this interval. According to this loss function, we got thecorresponding tangent SVM. The experimental results show that tangent SVM is less sensitive to outliersthan conventional SVM. So the proposed new loss function and tangent SVM are both effective.

摘要： To reduce the influences of outliers on support vector machine (SVM) classification problem, weconstructed a new tangent loss function. Since the tangent loss function is not smooth in some interval, asmoothing function was used to approximate it in this interval. According to this loss function, we got thecorresponding tangent SVM. The experimental results show that tangent SVM is less sensitive to outliersthan conventional SVM. So the proposed new loss function and tangent SVM are both effective.

摘要： A kind of soft sensing is proposed by combining empirical mode decomposition(EMD) with support vector machine optimized by improved particle swarm optimization(IPSO-SVM). EMD is a highly adaptive decomposition and can decompose anycomplicated signal into so called Intrinsic Mode Functions (IMF), which not only has excellent performance of feature extraction but can reduce the dimension of the model input data space. we can extracts IMF energy feature as therninput feature vectors of IPSO-SVM. Support vector machine (SVM) has been successfully employed to solve regression problem but it is difficult to select appropriate SVM parameters.A new SVM model based on adaptive particle swarm optimization (APSO) for parameter optimization is proposed which not only has strong global search capability, but also is very easy to implement. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that IPSO-SVM approach has a better prediction and generalization.

摘要： A kind of soft sensing is proposed by combining empirical mode decomposition(EMD) with support vector machine optimized by improved particle swarm optimization(IPSO-SVM). EMD is a highly adaptive decomposition and can decompose anycomplicated signal into so called Intrinsic Mode Functions (IMF), which not only has excellent performance of feature extraction but can reduce the dimension of the model input data space. we can extracts IMF energy feature as therninput feature vectors of IPSO-SVM. Support vector machine (SVM) has been successfully employed to solve regression problem but it is difficult to select appropriate SVM parameters.A new SVM model based on adaptive particle swarm optimization (APSO) for parameter optimization is proposed which not only has strong global search capability, but also is very easy to implement. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that IPSO-SVM approach has a better prediction and generalization.

摘要： A kind of soft sensing is proposed by combining empirical mode decomposition(EMD) with support vector machine optimized by improved particle swarm optimization(IPSO-SVM). EMD is a highly adaptive decomposition and can decompose anycomplicated signal into so called Intrinsic Mode Functions (IMF), which not only has excellent performance of feature extraction but can reduce the dimension of the model input data space. we can extracts IMF energy feature as therninput feature vectors of IPSO-SVM. Support vector machine (SVM) has been successfully employed to solve regression problem but it is difficult to select appropriate SVM parameters.A new SVM model based on adaptive particle swarm optimization (APSO) for parameter optimization is proposed which not only has strong global search capability, but also is very easy to implement. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that IPSO-SVM approach has a better prediction and generalization.