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Residual network(ResNet) is an effective instance and a significant extension of deep convolutional neural network. ResNet utilizes skip-connection between input layers and output layers to solve the vanishing gradient problem. Due to the powerfulness of skip-connection, the gradient can flow directly through the identity function from later layers to the earlier layers. However, skip-connection makes...
This paper discusses how to apply the ensemble learning for the individual learners on the randomly splitting data. Rather than letting the individual learners learn independently on the different subsets, it would be better for the individual learners to learn cooperatively by exchanging the learned values. In this way, the individual learners could learn the whole given data together while they...
Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation...
This paper proposes a hybrid negative correlation learning in which each individual neural network in an neural network ensemble would either learn a data point by negative correlation learning or learn to be different to the neural network ensemble. The implementation is through randomly splitting the training set into two subsets for each individual neural network in learning. On one subset of the...
Data mining can find some interest information from large amounts of data. Data association (association rules) can find associations among data items. Data classification distinguishes every data from a data set or group, and it also can combine data association. Formal concept analysis is a data analyzing theory which discovers concept structure in data sets. It can transform formal context into...
We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters...
Studies show that multiple modal biometric systems for small-scale populations perform better than single modal biometric systems for robots's recognition. This paper establishes a new fusion method for multiple biometric feature identification which combines visual with auditory information. Before the fusion, speaker recognition based on vector quantization and face recognition based on sparse representation...
To retrain an existing multilayer perceptron (MLP) on-line using newly observed data, it is necessary to incorporate the new information while preserving the performance of the network. This is known as the “plasticitystability” problem. For this purpose, we proposed an algorithm for on-line training with guide data (OLTA-GD). OLTA-GD is good for implementation in portable/wearable computing devices...
In the ensemble learning methods for training individual learners in a committee machine, two learning items should be optimized, including minimization of both the squared difference between the target and the learner's output and the estimated correlation between the learner and the rest of learners in the ensemble. The first term is to force each learner to learn the given data. The second term...
It is certain that the individual learners should be different from each other in order for a committee machine to reach the better performance. However, differences alone among the individual learners are not enough for the committee machine to predict well on the unknown data. It would be essential for each individual learner to be able to decide whether to learn to be different or not to the other...
In this paper, we propose a method for generating guide data, and investigate its efficiency and efficacy for on-line learning with guide data. On-line learning in this research updates a learning model initialized by the decision boundary making algorithm proposed by us in our earlier study. The problem is that, if the guide data are not properly generated, on-line learning may require high computational...
We compare the performance of multilayer perceptrons (MLPs) obtained using back propagation (BP), decision boundary making (DBM) algorithm and extreme learning machine (ELM), and investigate better method for developing aware agents (A-agent) that are suitable for implementation in portable/wearable computing devices (P/WCD). The DBM has been proposed by us for inducing compact and high performance...
Negative correlation learning is an ensemble learning approach that is able to create negatively correlated learners simultaneously and cooperatively in a committee machine. One problem in negative correlation learning is that the learning error functions are defined in the same way for all individual learners. Learners have little choice in making their own decisions on how to learn a given data...
Negative correlation learning has been proposed to create a set of negatively correlated artificial neural networks (ANNs) in a committee machine. In negative correlation learning, the error signals for each ANN on a given data are not only decided by the error differences between the output of ANN and the targets. Two terms are optimized at the same time. The first one is to minimize the error between...
Two different implementations of negative correlation learning with λ > 1 are discussed in this paper. In the first implementation, every learner is forced to learn to be different to the ensemble on every data point no matter what have been learned by the ensemble and itself. In the second implementation, every learner is selectively to learn to be different to the ensemble on every data point...
Different to independent and sequential learning, negative correlation learning trains all learners in an ensemble simultaneously and cooperatively with direct interactions. In negative correlation learning, each learner can be learned by the error signals only based on the differences between the output of the ensemble and the target output on a given example without considering whether itself has...
Self-awareness is a kind of ability of recognizing oneself as an individual being different from the environment and other individuals. This paper proposes negative correlation learning with self-awareness in order for each artificial neural network (ANN) in a committee machine to be self-aware in learning so that it could decide by itself to learn more or less. On one hand, when the learning would...
Different to other re-sampling ensemble learning, negative correlation learning trains all individual models in an ensemble simultaneously and cooperatively. In negative correlation learning, each individual could see all training data, and adapt its target function based on what the rest of individuals in the ensemble have learned. In this paper, two error bounds are introduced in negative correlation...
A simple algorithm for dynamically adjusting the tap-length of each branch of multi-branch linear equalizer is proposed. Simulation results show that the algorithm can adaptively adjust the tap-length of each branch according to the specific channel profile corresponding to each branch, and the advantages of the proposed algorithm against existing counterparts.
The plight of ethnic medical technology, education, and industry in ethnic minorities was examined, and the combined “industry-education-research” experience of Jiangxi University of Traditional Chinese Medicine was analyzed. Facing key scientific issues and technological demands, the development of an innovative collaborative model for ethnic medical technology, education, and industry was examined,...
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