A self-growing probabilistic decision-based neural network with automatic data clustering

Tseng, Cheng-Lung; Chen, Y.H.; Xu, Yeong-Yuh; Pao, Hsiao-Tien; Fu, Hsin-Chia

doi:10.1016/j.neucom.2004.03.002

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…For example, Melody in [4] initializes a neuron topology of small scale at first and then gradually expands it following the update of input data. Tseng et al in [5] improve this algorithm by tuning neuron topology in virtue of dynamically creating and deleting arcs between neurons.…”

Section: Introductionmentioning

confidence: 99%

A Novel Self-Adaptive Clustering Algorithm for Dynamic Data

Liu

Lin

Shan

et al. 2012

Neural Information Processing

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Abstract. Along with the fast advance of internet technique, internet users have to deal with novel data every day. For most of them, one of the most useful knowledge exploited from web is about the transfer of the information expressed by dynamically updated data. Unfortunately, traditional algorithms often cluster novel data without considering the existent clustering model. They have to cluster input data over again, once input data are updated. Hence, they are time-consuming and inefficient. For efficiently clustering dynamic data, a novel Self-Adaptive Clustering algorithm (abbreviated as SAC) is proposed in this paper. SAC comes from Self Organizing Mapping algorithm (abbreviated as SOM), whereas, it doesn't need to make any assumption about neuron topology beforehand. Besides, when input data are updated, its topology remodels meanwhile. Experiment results demonstrate that SAC can automatically tune its topology along with the update of input data.Keywords: Self-adaptive algorithm, Competitive learning, Minimum spanning tree, Self-organizing-mapping. IntroductionDue to the fast advance of internet technique, the data from web are unstable and dynamically updated at times (this kind of data is denoted as dynamic data in this paper). This phenomenon forces internet users to face to novel data anywhere and anytime. In general, via clustering dynamic data, it is easy to acquire the knowledge about, what information appears, what information disappears, and what information maintains. This kind of knowledge is essential to the men who need to make the decisions via observing dynamic data. As indicated by [1], there have been proposed many methods to cluster dynamic data as followings.Dhillon et al in [2] just propose a dynamic clustering algorithm to help analyze the transfer of information. Unfortunately, this algorithm is time-consuming and impractical, since it needs to run several times. Ghaseminezhad and Karami in [3] improve this algorithm by employing SOM structure, which forms an initial neuron topology at first and then dynamically tunes its topology once input data are updated.

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Section: Introductionmentioning

confidence: 99%

A Novel Self-Adaptive Clustering Algorithm for Dynamic Data

Liu

Lin

Shan

et al. 2012

Neural Information Processing

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“…Recently, efforts are made to take advantage of artificial intelligence techniques for watermark embedding and extraction (Pan et al 2007(Pan et al , 2004Chang et al 2007;Shieh et al 2004;Fu et al 2004;Wang et al 2006;Zhang and Zhang 2005;Zhang et al 2002;Xu et al 2007;Tseng et al 2004). Neural networks are introduced into watermarking (Wang et al 2006;Zhang and Zhang 2005;Zhang et al 2002;Xu et al 2007;Tseng et al 2004), which make the watermark detection more robust against common attacks.…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks are introduced into watermarking (Wang et al 2006;Zhang and Zhang 2005;Zhang et al 2002;Xu et al 2007;Tseng et al 2004), which make the watermark detection more robust against common attacks. Genetic algorithm is proposed for selection of the best embedding positions in block based DCT domain watermarking (Shieh et al 2004).…”

Section: Introductionmentioning

confidence: 99%

“…To ensure the watermark safety and imperceptibly, embedding the watermark bits into the edges and textures of the image we make use of the statistical properties of the dual-tree wavelet transform (DTCWT) and the human visual system (HVS). Due to neural network (Wang et al 2006;Zhang and Zhang 2005;Zhang et al 2002;Xu et al 2007;Tseng et al 2004) possessing the learning capability from given training patterns, our method can memorize the relations between a watermark and the corresponding watermarked image. The watermarked images are tested for different type of attacks and the results prove the validity of the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

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A new watermarking approach based on probabilistic neural network in wavelet domain

Wen

Zhang

et al. 2008

Soft Comput

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A novel scheme of digital image watermarking based on the combination of dual-tree wavelet transform (DTCWT) and probabilistic neural network is proposed in this paper. Firstly, the original image is decomposed by DTCWT, and then the watermark bits are added to the selected coefficients blocks. Because of the learning and adaptive capabilities of neural networks, the trained neural networks can recover the watermark from the watermarked images. Experimental results show that the proposed scheme has good performance against several attacks.

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“…Probabilistic networks have the distinguishing characteristic that they specify a probability distribution. They therefore have a clear semantics that allow them to be processed in order to do diagnosis [1], learning [2], explanation [3] and many other inference tasks necessary for intelligent systems.…”

Section: Introductionmentioning

confidence: 99%