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5th International Conference on Information Technology and Quantitative management (ITQM)

作者： Liu, Ying Xiang, Chao Univ Chinese Acad Sci Sch Comp & Engn Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

There're many effective architectures of the artificial neural network(ANN). For which the training is a hard work. The cost for training an ANN increases exponentially when the ANN gets deeper or wider. We therefore propose a novel architecture, the Hybrid Learning Network(HLN), to achieve a fast learning with good stablity. The HLN can learn from both labeled data and unlabeled data at the same time in a hybrid learning manner. It uses a Self Organizing Map unified by the specially designed nonlinear function as the sparsity mask for a hidden layer to improve the training speed. We experiment our architecture on a synthetic dataset to test its regression capability against the traditional architecture, the result is promising. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： hybrid learning neural network sparsity mask self organizing map fast learning

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A Novel data mining Approach Towards Human Resource Performance Appraisal 18th

A Novel Data Mining Approach Towards Human Resource Performa...

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18th International Conference on Computational Science (ICCS)

作者： Quan, Pei Liu, Ying Zhang, Tianlin Wen, Yueran Wu, Kaichao He, Hongbo Shi, Yong Univ Chinese Acad Sci Sch Comp & Control Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Renmin Univ China Sch Lab & Human Resources Beijing 100872 Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (纸本)9783319937014;9783319937007

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Traditional performance appraisal programs are labor-based, lacking of fairness. Furthermore, as globalization and technology advance, in order to meet the fast changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. This paper proposes a data mining-based performance appraisal framework, to conduct an automatic and comprehensive assessment of the employees on their working ability and job competency. This framework has been successfully applied in a domestic company, providing a reliable basis for its human resources management.

关键词： Performance appraisal data mining Enterprise strategy Job competency

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Analysis of the relation between artificial intelligence and the internet from the perspective of brain science

arXiv

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arXiv 2018年

作者： Liu, Feng Shi, Yong Li, Peijia Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Artificial intelligence (AI) like deep learning, cloud AI computation has been advancing at a rapid pace since 2014. There is no doubt that the prosperity of AI is inseparable with the development of the Internet. However, there has been little attention to the link between AI and the internet. This paper explores them with brain insights mainly from four views:1) How is the general relation between artificial intelligence and Internet of Things, cloud computing, big data and Industrial Internet from the perspective of brain science. 2) Construction of a new AI system model with the Internet and brain science. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Research on artificial intelligence ethics based on the evolution of population knowledge base

arXiv

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arXiv 2018年

作者： Liu, Feng Shi, Yong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

The unclear developmentdirection of human society is a deep reason for that it is difficult to form a uniform ethical standard for human society and artificial intelligence. Since the 21st century, the latest advances in the Internet, brain science and artificial intelligence have brought new inspiration to the researchon the developmentdirection of human society. Through the study of the Internet brain model, AI's IQ evaluation, and the evolution of the brain, this paper proposes that the evolution of population knowledge base is the key for judging the development direction of human society,thereby discussing the standards and norms for the construction of artificial intelligence ethics. Copyright © 2018, The Authors. All rights reserved.

关键词： knowledge based systems

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A nominal association matrix with feature selection for categorical data

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第16期46卷 7798-7819页

作者： Huang, Wenxue Shi, Yong Wang, Xiaogang Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Guangdong Peoples R China Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China York Univ Dept Math & Stat Toronto ON Canada

An intrinsic association matrix is introduced to measure category-to-variable association based on proportional reduction of prediction error by an explanatory variable. The normalization of the diagonal gives rise to the expected rates of error-reduction and the off-diagonal yields expected distributions of the rates of error for all response categories. A general framework of association measures based on the proposed matrix is established using an application-specific weight vector. A hierarchy of equivalence relations defined by the association matrix and vector is shown. Applications to financial and survey data together with simulation results are presented.

关键词： Association matrixl Association vector Categorical data Feature selection Proportional prediction The GK-tau

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A multi-kernel framework with nonparallel support vector machine

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NEUROCOMPUTING 2017年 266卷 226-238页

作者： Tang, Jingjing Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

Multiple kernel learning (MKL) serves as an attractive research direction in current kernel machine learning field. It can flexibly process diverse characteristics of patterns such as heterogeneous information or irregular data, non-flat distribution of high-dimensional samples etc. The existing MM. models are usually built on SVM. However, there is still potential to improve the performance of MKL instead of learning based on SVM. Nonparallel support vector machine (NPSVM), as a novel clasifier, pursues two nonparallel proximal hyperplanes with several incomparable advantages over the state-of-the-art classifiers. In this paper, we propose a new model termed as MKNPSVM for classification. By integrating NPSVM into the MKL framework, MKNPSVM inherits the advantages of them and opens a new perspective to extend NPSVM to the MKL field. To solve MKNPSVM efficiently, we provide an alternating optimization algorithm (Alter-MKNPSVM for short) as the solution. We theoretically analyze the performance of MKNPSVM from three viewpoints: the generalization capability analysis, the convergence analysis and the comparisons with NPSVM and MKL. Experimental results on eighteen publicly available UCI data sets confirm the effectiveness of our method. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multiple kernel learning Nonparallel support vector machine Alternating optimization

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Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network 24th

Partial Fingerprint Matching via Phase-Only Correlation and ...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Qin, Jin Tang, Siqi Han, Congying Guo, Tiande Univ Chinese Acad Sci Beijing Peoples R China UCAS Sch Math Sci Beijing Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9783319701363;9783319701356

A major approach for fingerprint matching today is based on minutiae. However, due to the lack of minutiae, their accuracy degrades significantly for partial-to-partial matching. We propose a novel matching algorithm that makes full use of the distinguishing information in partial fingerprint images. Our model employs the Phase-Only Correlation (POC) function to coarsely assign two fingerprints. Then we use a deep convolutional neural network (CNN) with spatial pyramid pooling to measure the similarity of the overlap areas. Experiments indicate that our algorithm has an excellent performance.

关键词： Partial fingerprint matching Phase-only correlation Polar Fourier transform Deep convolutional neural networks

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Algorithms for computing strong μ-bases for rational tensor product surfaces

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COMPUTER AIDED GEOMETRIC DESIGN 2017年 52-53卷 48-62页

作者： Shen, Li-Yong Goldman, Ron Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Rice Univ Dept Comp Sci 6100 Main StMS-132 Houston TX 77005 USA

Implicitizing rational surfaces is a fundamental computational task in Algorithmic Algebraic Geometry. Although the resultant of a mu-basis for a rational surface is guaranteed to contain the implicit equation of the surface as a factor, this resultant may also contain extraneous factors. Moreover, mu-bases for rational surfaces are, in general, notoriously difficult to compute. Here we develop fast algorithms to find mu-bases for rational tensor product surfaces whose resultants are guaranteed to be the implicit equation of the corresponding rational surface with no extraneous factors. We call these mu-bases strong mu-bases. Surfaces with strong mu-bases are relatively rare. We show how these strong mu-bases are related to the number of base points counting multiplicity of the corresponding surface parametrization. In addition, when the base points are simple, we provide tables of rational tensor product surfaces with strong mu-bases based on the bidegree of the rational surface and the number of base points of the parametrization. The bidegrees of the corresponding strong mu-bases are also listed in these tables. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Rational surface Base point Implicitization Moving plane Syzygy Strong mu-basis

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Cross-Modal Retrieval Using Multiordered Discriminative Structured Subspace Learning

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第6期19卷 1220-1233页

作者： Zhang, Liang Ma, Bingpeng Li, Guorong Huang, Qingming Tian, Qi Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

This paper proposes a novel method for cross-modal retrieval. In addition to the traditional vector (text)-to-vector (image) framework, we adopt a matrix (text)-to-matrix (image) framework to faithfully characterize the structures of different feature spaces. Moreover, we propose a novel metric learning framework to learn a discriminative structured subspace, in which the underlying data distribution is preserved for ensuring a desirablemetric. Concretely, there are three steps for the proposed method. First, the multiorder statistics are used to represent images and texts for enriching the feature information. We jointly use the covariance (second-order), mean (first-order), and bags of visual (textual) features (zeroth-order) to characterize each image and text. Second, considering that the heterogeneous covariance matrices lie on the different Riemannian manifolds and the other features on the different Euclidean spaces, respectively, we propose a unified metric learning framework integrating multiple distance metrics, one for each order statistical feature. This framework preserves the underlying data distribution and exploits complementary information for better matching heterogeneous data. Finally, the similarity between the different modalities can be measured by transforming the multiorder statistical features to the common subspace. The performance of the proposed method over the previous methods has been demonstrated through the experiments on two public datasets.

关键词： Cross-modal retrieval documents and images multimedia

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An interview with Professor Raj Reddy on Web Intelligence (WI) and Computational Social Science (CSS)

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WEB INTELLIGENCE 2018年第3期16卷 143-146页

作者： Zhong, Ning Liu, Jiming Shi, Yong Yao, Yiyu Maebashi Inst Technol Dept Life Sci & Informat Maebashi Gunma 3710816 Japan Beijing Univ Technol Int WIC Inst Beijing 100022 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada

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