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AdaBoost is a classic ensemble learning algorithm with good classifier performance. In the past, it mainly used weak classifier as base classifier, such as KNN. They are simple and easy to train, but the essence of the weak classifier, it is impossible to get very high classification accuracy. In order to improve the correct rate, this paper introduces the AdaBoost ensemble classifier based on convolutional...
Most sentiment classification for microblogs are based on supervised learning methods. The performance of these methods heavily relies on carefully chosen training datasets. These datasets usually cannot be too small. This is cumbersome and makes these methods less attractive for practical use. To address this problem, approaches to automatically generate training datasets have been proposed. However,...
One-Class Support Vector Machine (OCSVM) is a general approach for novelty detection in the fields of machine learning and pattern classification. At the same time, AdaBoost is a famous ensemble method which can improve the performance of its base classifiers. However, the base classifiers in the AdaBoost method prefer to be weak classifiers. Since OCSVM is regarded as a strong classifier, the traditional...
The ability to measure cognitive load level in real time is extremely useful for improving the efficiency of interfaces and contents delivering, especially when interfaces and contents get complex in a multimedia environment. Speech is highly suitable for measuring cognitive load due to its non-intrusive nature and ease of collection. In this paper, we investigated the patterns of prosodic features...
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