In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is i...
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In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.
Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph cons...
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With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets ...
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Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classica...
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Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classical algorithms (e.g. spectral subtraction, Wiener filtering and Bayesian-based enhancement), and more recently several deep neural network-based. In this paper, we propose a hybrid approach to speech enhancement which combines two stages: In the first stage, the well-known Wiener filter performs the task of enhancing noisy speech. In the second stage, a refinement is performed using a new multi-stream approach, which involves a collection of denoising autoencoders and auto-associative memories based on Long Short-term Memory (LSTM) networks. We carry out a comparative performance analysis using two objective measures, using artificial noise added at different signal-to-noise levels. Results show that this hybrid system improves the signal's enhancement significantly in comparison to the Wiener filtering and the LSTM networks separately.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend deco...
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The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as inpu...
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The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as input in such statistic models. In order to understand the impacts of direct solar radiance and wind speed on the model performance comprehensively, a model based on Elman neural networks (ENN) was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs. Compared with the measured heat demand, the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN and the inclusion of wind speed can improve the prediction accuracy of ENN. However, ENN cannot benefit from the introduction of both wind speed and direct solar radiation simultaneously.
Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The installation of smar...
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This paper presents a novel image abstraction tech- nique, the deceived bilateral filter (DBF). The DBF combines border sharpening with simplification of homogeneous regions to achieve moderate de-blurring and colour ...
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This paper presents a new dissimilarity measure for the detection of dynamic overlays in football games from TV broadcasting. The detection of the dynamic overlays is used for finding the replays in the input video, t...
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This paper presents the first module of our system ACE for the automated interpretation of association football games. This module is in charge of the temporal segmentation of the input broadcasting TV video into scen...
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ISBN:
(纸本)9781479908271
This paper presents the first module of our system ACE for the automated interpretation of association football games. This module is in charge of the temporal segmentation of the input broadcasting TV video into scenes. The temporal segmentation is based on a new devised dissimilarity measure using the Bhattacharyya distance of the hue and local spatial variance histograms, to incorporate chromatic and structural properties of the images. Great results of extended experiments conducted on the last FIFA's World Cup are presented.
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