To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identific...To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.展开更多
Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG...Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.展开更多
Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative ...Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.展开更多
在说话人识别研究中,基于身份认证向量(Identity vector,IVEC)的说话人建模方法可以有效地提取说话人信息,是目前处于国际前沿的建模方法.本文对身份认证向量后接支持向量机(Identity vector followed by support vector machine,IVEC-S...在说话人识别研究中,基于身份认证向量(Identity vector,IVEC)的说话人建模方法可以有效地提取说话人信息,是目前处于国际前沿的建模方法.本文对身份认证向量后接支持向量机(Identity vector followed by support vector machine,IVEC-SVM)的说话人识别系统进行了研究,对比了该系统在十种不同核函数下的识别性能,并与文献中身份认证向量后接余弦距离打分(Identity vector followed by cosine distance scoring,IVEC-CDS)系统进行了比较.在美国国家标准技术局(American National Institute of Standards and Technology,NIST)组织的2010年电话信道—电话信道说话人识别核心评测数据库上的实验结果显示,基于核函数的IVEC-SVM系统性能明显优于IVEC-CDS的系统性能.此外,实验结果表明基于Spline核的IVEC-SVM系统可取得最好的识别性能,与IVEC-CDS系统相比,其等错点(Equal error rate,EER)在分数归一化前后分别降低了10%和3%.展开更多
基金Sponsored by the National Natural Science Foundation of China(60472110)
文摘To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.
基金Key Program of Natural Science Foundation of Shandong Province(No.ZR2013FZ002)Program of Science and Technology of Suzhou(No.ZXY2013030)Independent Innovation Foundation of Shandong University(No.2012DX008)
文摘Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61271093,Grant 61471146, and the Program ofMinistry of Education for New Century Excellent Talents under Grant NCET-12-0150
文摘Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.
文摘在说话人识别研究中,基于身份认证向量(Identity vector,IVEC)的说话人建模方法可以有效地提取说话人信息,是目前处于国际前沿的建模方法.本文对身份认证向量后接支持向量机(Identity vector followed by support vector machine,IVEC-SVM)的说话人识别系统进行了研究,对比了该系统在十种不同核函数下的识别性能,并与文献中身份认证向量后接余弦距离打分(Identity vector followed by cosine distance scoring,IVEC-CDS)系统进行了比较.在美国国家标准技术局(American National Institute of Standards and Technology,NIST)组织的2010年电话信道—电话信道说话人识别核心评测数据库上的实验结果显示,基于核函数的IVEC-SVM系统性能明显优于IVEC-CDS的系统性能.此外,实验结果表明基于Spline核的IVEC-SVM系统可取得最好的识别性能,与IVEC-CDS系统相比,其等错点(Equal error rate,EER)在分数归一化前后分别降低了10%和3%.
