This book constitutes the refereed proceedings of the internationalconference on Artificial intelligence and Computational intelligence, AICI 2012, held in Chengdu, China, in October 2012. The 163 revised full papers...
ISBN:
(数字)9783642342400
ISBN:
(纸本)9783642342394
This book constitutes the refereed proceedings of the internationalconference on Artificial intelligence and Computational intelligence, AICI 2012, held in Chengdu, China, in October 2012. The 163 revised full papers presented were carefully reviewed and selected from 724 submissions. The papers are organized in topical sections on applications of artificial intelligence; applications of computational intelligence; data mining and knowledge discovering; evolution strategy; intelligent image processing; machine learning; neural networks; patternrecognition.
This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While common link analysis models are limited to sing...
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ISBN:
(纸本)9781467322010;9781467322034
This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While common link analysis models are limited to single-type link prediction, we attempt here to address the prediction of multiple relations, which we refer to as Link pattern Prediction (LPP) problem. For that we propose a Probabilistic Latent Tensor Factorization (PLTF) model and furnish the Hierarchical Bayesian treatment of the proposed probabilistic model to avoid overfitting problem. To learn the proposed model we develop an efficient Markov Chain Monte Carlo sampling method. Extensive experiments are conducted on several real world datasets and demonstrate significant improvements over several existing state-of-the-art methods.
The efficiency of multiple pattern matching is the core technology of Deep Packet Inspection and it is important to the real word of internet traffic analysis. In this research we propose a new multiple pattern matchi...
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ISBN:
(纸本)9781467322010
The efficiency of multiple pattern matching is the core technology of Deep Packet Inspection and it is important to the real word of internet traffic analysis. In this research we propose a new multiple pattern matching technique for the fixed pattern in the packet load, which is called I-HASH multiple various position pattern matching algorithm. It is used to improve the matching speed through the preprocessing. Proposed algorithm is implemented in our experiments and compared with the existing method. The results demonstrate that our algorithm is much more effective than STRCMP algorithm, especially when the number of patterns is quiet large. This will be extraordinarily helpful in the areas of Internet application identification system and network intrusion detection system and so on, which contain huge amounts of fixed patterns.
In the April 2010 Nature research report, it was announced that biological physicists only very recently discovered that there exists a leadership pattern in flocks of pigeon birds. The most authoritative birds of the...
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ISBN:
(纸本)9783642310201
In the April 2010 Nature research report, it was announced that biological physicists only very recently discovered that there exists a leadership pattern in flocks of pigeon birds. The most authoritative birds of the pigeons' flock take the lead, and followers follow the leaders' directions. Pigeon leaders' roles vary over time. Following this unprecedented discovery made by zoologists at the University of Oxford and Eotvos University, we extend in this paper the flocking model largely used in computer science. We define a new biologically inspired clustering algorithm entitled "FlockbyLeader" that detects hierarchical leaders, discovers their followers, and enables them to flock based on local proximity in an artificial virtual space to create clusters. We offer empirical evidence that the algorithm outperforms both the existing flocking algorithm and the K-means algorithm. We analyze the performance of the algorithm based on widely used datasets in the literature.
The article is mainly to study a patternrecognition method which is based on the local structure feature and spatial topological relationship modeling. From the perspective of human cognition and bio-mimetic pattern ...
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The background noise influences the face image recognition greatly. It is crucial to remove the noise signals prior to the face image recognition processing. For this purpose, the wavelet de-noising technology has com...
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ISBN:
(纸本)9783037855201
The background noise influences the face image recognition greatly. It is crucial to remove the noise signals prior to the face image recognition processing. For this purpose, the wavelet de-noising technology has combined with the kernel principal component analysis (KPCA) to identify face images in this paper. The wavelet de-noising technology was firstly used to remove the noise signals. Then the KPCA was employed to extract useful principal components for the face image recognition. By doing so, the dimensionality of the feature space can be reduced effectively and hence the performance of the face image recognition can be enhanced. Lastly, a support vector machine (SVM) classifier was used to recognize the face images. Experimental tests have been conducted to validate and evaluate the proposed method for the face image recognition. The analysis results have showed high performance of the newly proposed method for face image identification.
A common characteristic of collecting the ground truth for medical images is that multiple experts provide only partially coherent manual segmentations, and in some cases, with varying confidence. As the result, there...
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Aim at the problems occurring in a least square method model and a neural network model for flatness patternrecognition, a new approach of flatness patternrecognition based on the wavelet transform(WT) and probabili...
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ISBN:
(纸本)9783642340406
Aim at the problems occurring in a least square method model and a neural network model for flatness patternrecognition, a new approach of flatness patternrecognition based on the wavelet transform(WT) and probabilistic neural network(PNN) is proposed to meet the demand of high-precision flatness control for cold strip mill. Energy distribution at different scales of stress distribution within strip steel is derived by WT with which severed as feature vectors. Then the feature vectors act as input vectors of PNN for target classification. The energy vectors by WT can differentiate various stress distribution. The design of PNN is straightforward and does not depend on training. PNN is suitable for signal classification. The model is shown to fit the actual data precisely. The simulation results show that the speed and accuracy of the flatness patternrecognition model are obviously improved.
In this paper, we propose a very simple face recognition method. This method first exploits a linear combination of all the training samples to express the test sample. Then it evaluates the capability of each class i...
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ISBN:
(纸本)9781467325882;9781467325875
In this paper, we propose a very simple face recognition method. This method first exploits a linear combination of all the training samples to express the test sample. Then it evaluates the capability of each class in expressing the test sample and assigns the test sample to the class that has the strongest capability. Using the expression result, the proposed method can classify the testing sample with a high accuracy. though the proposed method exploits only one training sample per class to perform classification, it might obtain a better performance than the nearest feature space (NFS) method.
With the ever-increasing amounts of published materials being made available, developing efficient means of locating target items has become a subject of significant interest. Among the approaches adopted for this pur...
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ISBN:
(纸本)9780769547749;9781467322621
With the ever-increasing amounts of published materials being made available, developing efficient means of locating target items has become a subject of significant interest. Among the approaches adopted for this purpose is word spotting, which enables the identification of documents through the use of pertinent keywords. This paper reports on an effective method of word spotting for Arabic handwritten documents that takes into consideration the nature of Arabic handwriting. Parts of Arabic Words (PAWs) form the basic components of this search process, and a hierarchical classifier (consisting of a set of classifiers each trained on a different part of the input pattern) is implemented. For the first time in Arabic word spotting, language models are incorporated into the process of reconstructing words from PAWs. Details of the method and promising experimental results are also presented.
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