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An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is...
Preserving sample's pair wise similarity is essential for feature selection. In supervised learning, labels can be used as a direct measure to check whether two samples are similar with each other. In unsupervised learning, however, such similarity information is usually unavailable. In this paper, we propose a new feature selection method through spectral clustering based on discriminative information...
Visually perceiving human motion at semantic level is an important however challenging problem in multimedia area. In this work, we propose a novel approach to map the low-level responses from visual detection to semantically sensitive description to human actions. The feature map is triggered by the output of deformable part model detection, in which the critical information about body parts configuration...
In this paper, we proposed an advanced face analysis platform for large-scale consumer photos, namely PFAP. Leveraging Client/Server architecture, the platform provides users high-performance face clustering and near-real time image retrieval service. Advanced face analysis schema, two-level parallel computing architecture and analysis as a service are three key innovations in PFAP. In face analysis...
Feature extraction of EEG signals plays an important role for classifying spontaneous mental activities in EEG-based brain computer interface (BCI). For the non-stationary nature of EEG data makes necessary some kind of adaptation of the BCI system, an improved feature extraction method based on multivariate adaptive autoregressive (MVAAR) models is proposed and applied to the classification of Motor...
This paper, a method of signal transformation for feature extraction is proposed. It can transform log-signal space into the vector space, which the experiment system requires, and then use SVM (Support Vector Machine) automatically to identify the water-flooded status of oil-saturated stratum. The results of experiment indicate that this algorithm has good identification ability and strong generalization...
It's proved that Nivrepsilas algorithm causes some errors for determining the right-side dependents. In Chinese, only verbs and prepositions have right-side dependents. Jin proposed a two-phase parser to solve verbs' problems. In this paper, we present a method to solve prepositions 'problems. Experimental results show that our approach achieves higher accuracy than previous approach.
This paper proposes a novel multi-class cluster support vector machine, which borrows ideas of nonparallel hyperplanes from generalized eigenvalue support vector machines. For a k-class classification problem, it trains k nonparallel hyperplanes respectively, and each one lies as close as possible to self-class while apart from the rest classes as far as possible. Then, the label of a new sample is...
This paper presents a two-step dependency parser to parse Chinese deterministically. By dividing a sentence into two parts and parsing them separately, the error accumulation can be avoided effectively. Previous works on shift-reduce dependency parser may guarantee the greedy characteristic of deterministic parsing less. This paper improves on a kind of deterministic dependency parsing method to weaken...
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