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Adaptive beamforming plays an important role in smart antenna systems. Nonblind adaptive algorithm utilizes the known training sequence to update the weights of antenna array at the cost of low transmission efficiency. Without sending training sequences, blind adaptive algorithm is a solution to implement beamforming and increase the transmission efficiency. Two classic blind algorithms are studied,...
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...
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...
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...
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...
Since fault diagnosis of blast furnace is very important in manufacturing, in this paper, a new strategy based on clustering combining SVMs pruned binary tree is proposed to solve diagnosis problem in blast furnace. According to the relations of categories in multi-class problem, it is needless to distinguish all the sorts. In order to improve classification efficiency, advantage of clustering and...
Aiming at the problem of Tibetan speech recognition under the condition of resistance from noise, a kind of Tibetan speech recognition algorithm, combining RBF network with auditory feature was presented in this paper. The description for the Tibetan speech signals was carried out with Mel frequency cepstrum constant (MFCC), and the recognition classifier was designed based on RBF network with the...
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...
Ear recognition is proved to be a promising authentication technique. Because of earpsilas special physiological structure and location, the fusion of ear and face biometrics could fully utilize their connection relationship of physiological location and the supplement between these two biometrics, and possess the advantage of recognizing people without their cooperation. In this paper a novel feature...
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