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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...
Recent studies have indicated that well-designed convolutional neural network (CNN) has achieved comparable performance to the spatial rich models with ensemble classifier (SRM-EC) in digital image steganalysis. In this paper, we discuss the difference and correlation between a CNN model and a SRM-EC model, and explore the classification error rate varying with texture complexity of an image for both...
In this paper, the use of mutual information and the Learn++.NSE algorithm is proposed to create an EEG SSVEP BCI system that can select and utilize data sets originating from a group of users. In typical BCI systems, the nonstationarity in the EEG prevents the system from blindly applying training data from other users to the incoming data. Mutual information is introduced to select previous data...
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...
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...
Fingerprint recognition systems are vulnerable to spoof attacks, which consist in presenting forged fingerprints to the sensor. Typical anti-spoofing mechanism is fingerprint liveness detection. Existing liveness detection methods are still not robust to spoofing materials, datasets and sensor variations. In particular, the performance of a liveness detection algorithm remarkably drops upon encountering...
In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR). First, we study an approach where uni-modal deep networks are trained separately and their final hidden layers fused to obtain a joint feature space in which another deep network is built. While the audio network alone achieves a phone error...
Standard Symbolic Aggregation Approximation (SAX) is at the core of many effective time series data mining algorithms. Its combination with Bag-of-Patterns (BoP) has become the standard approach with state-of-the-art performance on standard datasets. However, standard SAX with the BoP representation might neglect internal temporal correlation embedded in the raw data. In this paper, we proposed time...
Facial gender recognition plays an important role in various industrial applications such as human-computer interaction and targeted advertising. Although several methods have been applied to facial gender recognition, it is still considered as a challenging problem. In this paper, a system based on optimal trade-off (OT) — Maximum average correlation height (MACH) filter is developed for facial gender...
Balanced ensemble learning is developed from negative correlation learning by shifting the learning targets. Compared to the negative correlation learning, balanced ensemble learning is able to learn faster and achieve the higher accuracy on the training sets for a number of the tested classification problems. However, it has been found that the higher accuracy balanced ensemble learning obtained...
Ensemble learning system could lessen the degree of overfitting that often appear in the supervised learning process for a single learning model. However, overfitting had still been observed in negative correlation learning that is an ensemble learning method with correlation-based penalty. Two constraints were introduced into negative correlation learning in order to conquer such overfitting. One...
Ensemble pruning aims to increase efficiency by reducing the number of base classifiers, without sacrificing and preferably enhancing performance. In this brief, a novel pruning paradigm is proposed. Two class supervised learning problems are pruned using a combination of first- and second-order Walsh coefficients. A comparison is made with other ordered aggregation pruning methods, using multilayer...
Gene selection is a crucial step when building a classifier from microarray or metagenomic data. As the number of observations is small, the gene selection tends to be unstable. It is common that two gene subsets, obtained from different datasets but dealing with the same classification problem, do not overlap significantly. Although it is a crucial problem, few works have been done on the selection...
It has been proved that there is a bias-variance-covariance trade-off among the trained neural network ensembles. In this paper, extra learning on random data points was proposed to control the variations of the correlations in the negative correlation learning (NCL). Without the control of the correlations, NCL might have arbitrary values on the unknown data points after learning too much on the...
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