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Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT). SVM is powerful for the problem with small samples, non linear and high dimension. A multi-class SVM classifier is applied to predict the coal and gas outburst in the paper. In this model, the dominant factors are the input vectors and the degree of outburst danger is divided into four types:...
The human face is a rich source of information for the viewer and facial expressions are a major component in judging a person's affective state, intention and personality. Facial expressions are an important part of human-human interaction and have the potential to play an equally important part in human-computer interaction. This paper evaluates various active appearance model (AAM) fitting methods,...
Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have recently been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of simple statistical models. In this work, we have developed a predictive model based...
We present a method to detect characters on signboards in natural scene images. For many applications, both classifier with small computational cost and the efficient feature set, which gives rise to accurate recognition are required. Texture based features are often used for target detection. It has been also shown that the shape of the intensity distribution is often useful for character extraction...
The human voice is primarily a carrier of speech, but it also contains non-linguistic features unique to a speaker and indicative of various speaker demographics, e.g. gender, nativity, ethnicity. Such characteristics are helpful cues for audio/video search and retrieval. In this paper, we evaluate the effects of various low-, mid-, and high-level features for effective classification of speaker characteristics...
The algorithm based on multi-feature and SVM is proposed. The paper firstly uses wavelet de-noising for gait images. The text offers to use width descriptors as gait features and combines lower angle features. The kernel-based Fisher criterion and support vector machine is combined to classification and identification. The gait characteristic is extracted by KFDA, which can obtain the best projection...
In this paper, we propose the so-called ldquoSVM'ed-kernel functionrdquo and its use in SVM classification problems. This kernel function is itself a support vector machine classifier that is learned statistically from data. We show that the new kernel manages to change the classical methodology of defining a feature vector for each pattern. One will only need to define features representing the similarity...
In this paper, we propose a novel static hand gesture recognition method, which is based on a new support vector machine (abbreviated as SVM) classifier. SVM is a classification method based on statistics theory. Typical SVMs can be sufficient to deal with small scale data, but these methods cause a lot of computation in quadratic programming while dealing with non-linear problems. SVM combined with...
The method of stable random projections is an efficient tool for computing the lalpha distances using low memory, where 0 < alpha les 2 may be viewed as a tuning parameter. This method boils down to a statistical estimation task and various estimators have been proposed, based on the geometric mean, harmonic mean, and fractional power etc. This study proposes the optimal quantile estimator, whose...
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