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A variety of problems are related to real-world gesture recognition, such as continuous data streams, concept drift, novel and outlier samples, noise, scarcity of manually labeled data, on-line classification and the fact that the same gesture may implement in different way. Two important features should be included in the classifier to overcome these problems, which are the ability of detecting the...
The HMM/SVM-based two-stage framework has been widely used for automatic phone alignment. The two-stage method uses SVM classifiers to refine the hypothesized boundaries given by the HMM-based Viterbi forced alignment. However, there are two drawbacks in using the classification model for detecting the phone boundaries. First, the training data contains only information about the boundary and far...
In this paper we present a method for the selection of training instances based on the classification accuracy of a SVM classifier. The instances consist of feature vectors representing short-term, low-level characteristics of music audio signals. The objective is to build, from only a portion of the training data, a music genre classifier with at least similar performance as when the whole data is...
Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of...
In a SVM classifier, the training speed is sensitive to the quantity of dataset. Therefore, the methodology of choosing some useful data that can decrease the number of training data and accelerate the training speed is usually a topic to be discussed on the SVM data process. The hyperplane of SVM is constructed by a small number of vectors. These vectors, whose locations are distributed in other...
When building a classifier from clean training data for a particular test environment, knowledge about the environmental noise and channel should be taken into account. We propose training a support vector machine (SVM) classifier using a modified kernel that is the expected kernel with respect to a probability distribution over channels and noise that might affect the test signal. We compare the...
Record matching, which identifies the records that represent the same real-world entity, is an important step for data integration. Most state-of-the-art record matching methods are supervised, which requires the user to provide training data. These methods are not applicable for the Web database scenario, where the records to match are query results dynamically generated on-the-fly. Such records...
Support vector machine (SVM) is a widely used tool in classification problem. SVM solves a quadratic optimization problem to decide which instances of training dataset are support vectors, i.e., the necessarily informative instances to form the classifier. The support vectors are intact tuples taken from the training dataset. Releasing the SVM classifier to public use or shipping the SVM classifier...
Pattern recognition may be used for crack size and type classification in ultrasonic nondestructive evaluation. Feature selection and reduction of computational complexity are two important problems to be solved in the development of pattern recognition algorithms. This paper describes a classifier based on support vector machines (SVM) and principal component analysis (PCA). The proposed approach...
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