Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Wang, Shitong; Jiang, Yizhang; Chung, Fu-Lai; Qian, Pengjiang

doi:10.1016/j.asoc.2015.07.040

Cited by 50 publications

(16 citation statements)

References 39 publications

(62 reference statements)

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“…Our previous work [35] reveals that a TSK fuzzy system can be equivalent to a fuzzy neural network, so CSK-TSK-FS can also be considered as a special neural network shown in Fig.4, where only one feature is involved in each yellow node of the hidden layer. In [8], [9], authors demonstrate that the optimization of a single-layer feedforward neural network is equivalence to solving a ridge regression problem that can be fast solved by the least learning machine (LLM). Therefore, obviously, the neural network in Fig.4 can also be fast solved by LLM only by augmenting original input features into the hidden layer.…”

Section: Training Algorithm Of Csk-tsk-fsmentioning

confidence: 99%

Common and Special Knowledge-Driven TSK Fuzzy System and Its Modeling and Application for Epileptic EEG Signals Recognition

et al. 2019

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Takagi-Sugeno-Kang (TSK) fuzzy systems are well known for their good balances between approximation accuracy and interpretability. Among a wide variety of existing TSK fuzzy systems, most of them are driven by special knowledge since the learned parameters of each fuzzy rule are totally different. However, common knowledge is equally important and useful in practice and hence a TSK fuzzy system embedded with common knowledge should be more intuitive and interpretable when tackling with realworld problems. In this paper, we propose a common and special knowledge-driven TSK fuzzy system (CSK-TSK-FS), in which the parameters corresponding to each feature in then-parts of fuzzy rules always keep invariant and these parameters are viewed as common knowledge. As for its modeling, except the gradient descent techniques and other existing training algorithms, we can obtain a trained CSK-TSK-FS from a trained GMM or a trained FLNN because the proposed fuzzy system CSK-TSK-FS is mathematically equivalent to a special GMM and a FLNN. CSK-TSK-FS has three characteristics: (1) with the classical centroid defuzzification strategy, the involved common knowledge can be separated from fuzzy rules such that the interpretability of CSK-TSK-FS can be enhanced; (2) it can be trained quickly by the proposed LLM-based training algorithm; (3) the equivalence relationships among CSK-TSK-FS, GMM and FLNN allow them to share some commonality in training such that the proposed LLM-based training algorithm provides a novel fast training tool for training GMM and FLNN. Experimental results on UCI, KEEL and epileptic EEG datasets demonstrate the promising classification of CSK-TSK-FS.

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Section: Training Algorithm Of Csk-tsk-fsmentioning

confidence: 99%

Common and Special Knowledge-Driven TSK Fuzzy System and Its Modeling and Application for Epileptic EEG Signals Recognition

et al. 2019

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“…In terms of algorithms, various machine-learning models, such as Naïve Bayes [15,16], Ensemble [17], or Deep Learning Structure [18] have been used for crime prediction, but Deep Neural Networks (DNN) provided better results in our previous experiments. This study uses DNN because it reflects representation learning and has been used in crosslingual transfer [19], speech recognition [20][21][22][23], image recognition [24][25][26][27], sentiment analysis [28][29][30][31][32], and biomedical [33]. Although the upper bound of the prediction performance still depends on the problem and the data themselves, DNN's auto-feature extraction [34] allows us to use rapid model building without feature processing, thus reducing the application threshold due to feature processing.…”

Section: Related Workmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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“…Thus, accessing the concealed data without knowing the topology of the NN appears practically infeasible [ 1 ]. Although some researchers prefer models with interpretability power such as explicit mathematical or statistical models or even heuristically encoded models such as fuzzy models, it has been proved that black box type of models when learning is feasible have more capability of capturing complicated knowledge and proving functionality in real world type of systems [ 2 ][ 3 ][ 4 ]. Such black box models have dramatically proved high efficiency in the state of the art of speech recognition, visual object recognition and many other fields [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Extreme learning machine based optimal embedding location finder for image steganography

et al. 2017

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In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

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Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Cited by 50 publications

References 39 publications

Common and Special Knowledge-Driven TSK Fuzzy System and Its Modeling and Application for Epileptic EEG Signals Recognition

Common and Special Knowledge-Driven TSK Fuzzy System and Its Modeling and Application for Epileptic EEG Signals Recognition

Grid-Based Crime Prediction Using Geographical Features

Extreme learning machine based optimal embedding location finder for image steganography

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