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A fast classification method using SVM based on affinity propagation clustering algorithm

Journal of Computational information Systems 2015年第16期11卷 6073-6080页

作者： Xu, Li Ding, Shifei Huang, Huajuan School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

The classification speed of SVM is inversely proportional to the number of Support Vectors (SVs). Therefore, the less SVs mean the more sparseness and the higher classification speed. In order to reduce the number of SVs but without lossing of generalization performance, a new algorithm called Fast Classification Method Using SVM Based on Affinity Propagation clustering algorithm (FCSVM-AP) is proposed, which employs Affinity Propagation (AP) clustering algorithm to cluster the original SVs and the cluster centers are used as the new SVs, then aiming to minimize the classification gap between SVM and FCSVM-AP, a quadratic programming model is built for obtaining the optimal coefficients of the new SVs. Meanwhile, it is proven that when clustering the original SVs, the minimal upper bound of the error between the original decision function and the fast decision function can be achieved by AP. Finally, experiments show that compared with original SVs, the number of SVs decreases and the speed of classification increases using FCSVM-AP, while the loss of accuracy is in the acceptable level. © 2015 by Binary information Press

关键词： Quadratic programming

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A hybrid deep learning CNN-ELM model and its application in handwritten numeral recognition

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Journal of Computational information Systems 2015年第7期11卷 2673-2680页

作者： Guo, Lili Ding, Shifei School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Convolutional Neural Network (CNN) is a typical algorithm structure of deep learning and it has applied in image recognition field widely. Based on CNN, this paper puts forward a novel hybrid deep learning model CNN-ELM. In this model, CNN is in charge of feature extraction and Extreme Learning Machine (ELM) performs as a classifier to complete the final classification so as to integrate the synergy of two classifiers. We have took experiments on MNIST digit database. Comparisons with CNN and ELM on the same database, CNN-ELM achieved an error rate of 0.67%, which indicate that CNN-ELM has achieved better generalization performance. ©, 2015, Binary information Press. All right reserved.

关键词： Character recognition

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Exploiting Feature Hierarchies with Convolutional Neural Networks for Cultural Event Recognition

Exploiting Feature Hierarchies with Convolutional Neural Net...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Mengyi Liu Xin Liu Yan Li Xilin Chen Alexander G. Hauptmann Shiguang Shan Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China School of Computer Science Carnegie Mellon University USA

Cultural events are kinds of typical events closely related to history and nationality, which play an important role in cultural heritage through generations. However, automatically recognizing cultural events still remains a great challenge since it depends on understanding of complex image contents such as people, objects, and scene context. Therefore, it is intuitive to associate this task with other high-level vision problems, e.g., object detection, recognition, and scene understanding. In this paper, we address this problem by combining both ideas of object / scene contents mining and strong image representation via CNN into a whole framework. Specifically, for object / scene contents mining, we employ selective search to extract a batch of bottom-up region proposals, which are served as key object / scene candidates in each event image, while for representation via CNN, we investigate two state-of-the-art deep architectures, VGGNet and GoogLeNet, and adapt them to our task by performing domain-specific (i.e., event) fine-tuning on both global image and hierarchical region proposals. These two models can complementarily exploit feature hierarchies spatially, which simultaneously capture the global context and local evidences within the image. In our final submission for ChaLearn LAP Challenge ICCV 2015, nine kinds of features extracted from five different deep models were exploited and followed with two kinds of classifiers for decision level fusion. Our method achieves the best performance of mAP=0.854 among all the participants in the track of cultural event recognition.

关键词： Cultural differences Feature extraction Proposals Image recognition Visualization Search problems Neural networks

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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Ranking Sentences in Scientific Literatures

Ranking Sentences in Scientific Literatures

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Jiao Tian Mengyun Cao Jin Liu Xiaoping Sun Zhuge Hai University of Chinese Academy of Sciences Beijing China Knowledge Grid Group CAS Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Wuhan University CAS Key Lab. of Intell. Inf. Process. Institute of Computing Technology Chinese Academy of Sciences Beijing CN

Sentence ranking is one of the most important research issues in text analysis. It can be used in extracted textual summarization and information retrieval. Graph-based methods are a common way of ranking and extracting sentences. In graph based methods, sentences are nodes of graph and edges are built based on the sentence similarities or on sentence co-occurrence relationship. PageRank style algorithms can be applied to get sentence ranks. In this paper, we focus on how to rank sentences in a single scientific paper. A scientific literature has more structural information than general texts and this structural information has not been fully explored yet in graph based ranking models. We investigated several different methods that used structural information such as paragraph and section information to construct a heterogeneous graph for ranking sentences. We conducted experiments on these methods to compare the results on sentence ranking. It shows that structural information can help identify more representative sentences.

关键词： Semantics Algorithm design and analysis Web pages Data mining Iterative methods Current measurement

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Motivation Learning in Mind Model CAM

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International Journal of Intelligence Science 2015年第2期5卷 63-71页

作者： Zhongzhi Shi Gang Ma Xi Yang Chengxiang Lu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Engineering Qufu Normal University Rizhao China

Motivation learning aims to create abstract motivations and related goals. It is one of the high-level cognitive functions in Consciousness And Memory model (CAM). This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process. The simulation of cyborg rat maze search shows that the motivation learning algorithm can adapt agents’ behavior in response to dynamic environment.

关键词： Motivation Learning Motivation processing Consciousness and Memory Model Cyborg Rat

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QACtools: A Quality Assessment and Quality Control Tool for Next-Generation Sequencing Data

QACtools: A Quality Assessment and Quality Control Tool for ...

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2015年中国智能自动化学术会议

作者： Dandan Song Ning Li Lejian Liao Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology

In recent years, next-generation sequencing technologies have been widely used, and are rapidly changing the landscape of genetics with unimaginable speed. However, the preliminary quality control regulations including trimming adaptor sequences, quality statistics, and depth and coverage statistics need to be applied to raw reads before further analysis. QACtools implements various utilities for processing raw reads in the FASTQ format and alignments in the BAM *** analyzes some relevant properties of next-generation sequencing data such as average read length, base quality scores, and so on. Additionally, to our knowledge,none of the other tools support depth and coverage statistic that have been provided in our tool for analysis of large DNA sequencing data. It is crucial for overcoming errors in base calling and assembly. We suppose tool is useful for the quality control of NGS data to perform further analysis. This software is an open source application freely availab.e at https://***/projects/qactools/.

关键词： NGS data analysis Quality control FASTQ format BAM format

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Video saliency prediction through machine learning with semantic information

Video saliency prediction through machine learning with sema...

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IEEE China Summit & International Conference on Signal and information processing (ChinaSIP)

作者： Xiaohui Fu Li Su Lei Qin University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Lab on Big Data Mining and Knowledge Management Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9781479919499

Saliency prediction is valuable in many video applications, such as intelligent retrieval, advertisement design and delivering, video coding and video summarization generating. Although image saliency is well explored, less works have been done on videos. Compared to images, the semantic orientation is more obvious for video saliency. In this paper, we propose a method to predict video saliency by introducing semantic information. Different from existing approaches, we simultaneously consider the bottom-up and top-down factors in a machine learning framework and utilize a semantic object learning model to compute the semantic related saliency map. The proposed method is tested on two datasets. The experiment results show that the proposed method keeps higher consistent with human's gaze tracks data on various video contents. Furthermore, the computation efficiency is also improved as we don't need to process every pixel of each frame during prediction features extraction.

关键词： Decision support systems Feature extraction Semantics Training Videos Indexes Training data

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A Study on Apparent Age Estimation

A Study on Apparent Age Estimation

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Yu Zhu Yan Li Guowang Mu Guodong Guo Lane Department of CSEE West Virginia University Morgantown WV USA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from?the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age lab.ls are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are?not many face images availab.e with annotated age lab.ls. Further, the annotated age lab.ls for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age lab.ls to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age lab.ls, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.

关键词： Estimation Face Databases Training Neural networks Data models Computer architecture

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Corrections to “Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation” [Jun 16 1201-1216]

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IEEE Transactions on Multimedia 2016年第10期18卷 2127-2127页

作者： Yan Hua Shuhui Wang Siyuan Liu Anni Cai Qingming Huang School of Information Engineering Communication University of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Smeal College of Business Pennsylvania State University University Park PA USA Center of Cloud Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Shenzhen China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intellectual Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Presents corrections to the paper, "Cross-modal correlation learning by adaptive hierarchical semantic aggregation," (Hua, Y., et al) IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1201-1216, Jun. 2016.

关键词： Semantics Learning (artificial intelligence) Ontologies Query processing

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