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Gradient boosting tree (GBT), a widely used machine learning algorithm, achieves state-of-the-art performance in academia, industry, and data analytics competitions. Although existing scalable systems which implement GBT, such as XGBoost and MLlib, perform well for datasets with medium-dimensional features, they can suffer performance degradation for many industrial applications where the trained...
Super-resolution provides effective prior information for the single-frame super resolution reconstruction. It's difficult to recover fine grained details via a general dictionary, trained through the diversified training samples, due to the negli-gence of structural characteristics. Thus, the dictionary whic-h is adaptive to local structures is needed. Considering the eigenvalues of structure tensor...
This paper presents a novel star pattern recognition algorithm based on a discrete hidden Markov model (HMM) for autonomous spacecraft attitude determination in the lost-in-space mode. A two-layer structure is proposed to build an HMM-based star pattern for every guide star. The hidden layer describes the unique geometric distributions of stars in the field of view via the transitions among hidden...
Data sparseness is a well-known problem for statistical machine translation (SMT) when morphologically rich and highly inflected languages are involved. This problem become worse in resource-scarce scenarios where sufficient parallel corpora are not available for model training. Recent research has shown that morphological segmentation can be employed on either side of the translation pair to reduce...
Mispronunciation detection is one of the vital tasks of the CALL (Computer Assisted Language Learning) systems. Many methods have been introduced to accomplish this task. However, few of them have addressed the detection task on confusable phones. In this paper, phone-level classifiers are utilized to improve the detection performance on the confusable phones. Features of the classifiers are posterior...
This paper presents an effective method for automatic pronunciation evaluation, which is based on feature extraction and combination. The proposed system extracts different kinds of evaluation features and combines them to produce an ultimate machine score, which predicts the overall pronunciation quality of a student. Experiments on a reading speech database show that most of the selected features...
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