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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...
The method on gene classification has been widely studied with the development of gene chip. Machine learning is the best choice to research the issue. But both traditional SVM and ELM cannot fulfill the requirement of high accuracy and short time. Therefore, in this paper, we propose a novel Accuracy Adaptive Extreme Learning Machine (A2-ELM) which can cover the shortage of traditional SVM and ELM...
This paper studies stability issues of sensorless torque control of high power interior permanent magnet synchronous machines (IPMSMs) under large load transients, e.g., complete torque reversals with the highest slew rate allowed. To perform a sensorless vector control, a current model-based sliding-mode observer is utilized to estimate the rotor position. The correlation between current regulation...
Non-myopic active learning allows the learner to select multiple unlabeled samples at a time. It avoids tedious retraining with each selected sample, and is effective to utilize multiple labelers. But current non-myopic active learning methods are typically greedy by selecting top N unlabeled samples with maximum score. While efficient, such a greedy active learning approach cannot guarantee the learner's...
An improved feature-level fusion algorithm based on kernel canonical correlation analysis is presented and applied to multimodal recognition based on fusion of ear and profile face in this paper. The fusion of ear and face biometrics could fully utilize their connection relationship of physiological location, and possess the advantage of recognizing people without their cooperation. First, only the...
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...
Active learning methods seek to reduce the number of labeled instances needed to train an effective classifier. Most current methods are myopic, i.e. select a single unlabelled sample to label at a time. The batch-mode active learning methods, on the other hand, typically select top N unlabeled samples with maximum score. Such selected samples often cannot guarantee the learner's performance. In this...
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...
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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