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Many real world classification problems lack of a large number of labeled data for learning an effective classifier. Active learning methods seek to address this problem by reducing the number of labeled instances needed to build an effective classifier. Most current active learning methods, however, are myopic, i.e. select one single unlabelled sample to label at a time. Obviously, such a strategy...
BCI research has undergone tremendous growth in recent years. For BCI however, there is often not enough quality brain data since data acquisition is time-consuming and the acquired data is typically noisy. In this research, we introduce a new active learning strategy to address this difficulty. Active learning methods seek to reduce the numberof labeled instances needed to train an effective classifier...
Active learning methods seek to reduce the number of labeled instances needed to train an effective classifier. Most current methods assume the availability of some reasonable amount of initially labeled training data so that the learners can be trained with sufficient quality. However, for many applications, the amount of initial training data is often limited, this will affect the quality of the...
In the process of building speech recognition models, accurate labeling of speech utterances is extremely time consuming and requires trained linguists. For fast building the speech recognition models in some industrial applications, we present a novel sample selection strategy that can use very few labeled speech utterances to construct the effective recognition model. The experimental results show...
In the researches on Tibetan language speech recognition, accurate labeling of Tibetan speech utterances is extremely time consuming and requires trained linguists. For alleviate this problem, we present an approach that can use few labeled Tibetan speech utterances to construct the effective recognition model. The experimental results show that our approach has better performance than traditional...
Because it is time-consuming and costly to annotate the large vocabulary Tibetan language corpus, it is not suitable to directly adopt the traditional automatic speech recognition (ASR) methods such as Hidden Markov Model (HMM), Dynamic Bayesian Networks (DBN), Artificial Neural Network (ANN). Thus, active learning can reduce annotation cost by sample selection. This paper proposed a new method to...
MBBNTree algorithm, which integrates the advantage of Markov blanket Bayesian networks (MBBN) and decision tree, would behave better performance than other Bayesian networks for classification. But the available training samples with actual classes are not enough for building MBBNTree classifier in practice. Active learning aims at reducing the number of training examples to be labeled by automatically...
The available cases with actual classes are not enough for building telecom clientspsila credit classification model in practice, especially for the newly established system in which old customerspsila data do not exist. For evaluating telecom clientspsila credit, a classifier based on active learning is proposed in this paper. Active learning aims at reducing the number of training examples to be...
MBBCTree algorithm, which integrates the advantage of Markov blanket Bayesian networks (MBBC) and Decision Tree, performances better than other Bayesian Networks for classification. But MBBCTree classifier was built by the traditional passive learning. The available training samples with actual classes are not enough for passive learning method for modelling MBBCTree classifier in practice. Active...
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