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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Guo, Xin-Chen Chen, Zhou-Yi Teng, Li-Rong Wu, Chun-Guo Du, Tian-Bao Lu, Jia-Hui Meng, Qing-Fan Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Science NortheastChinaInstituteofElectricPowerEngineering Jilin 132012 China Institute of General Development and Evaluation of Equipment EAAF PLA Beijing 100076 China College of Life Science Jilin University Changchun 130012 China

ISBN: (纸本)078039092X

This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm performance. Simulation results show that the proposed method obtains high quality precision in the generalization, compared with multiple linear regression, and that it is an efficient approach to regression estimation. © 2005 IEEE.

关键词： Medicine

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AN IMPROVED BICLUSTERING ALGORITHM AND ITS APPLICATION TO GENE EXPRESSION SPECTRUM ANALYSIS

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Genomics, Proteomics & Bioinformatics 2005年第3期3卷 189-193页

作者： Hua Qu Liu-Pu Wang Yan-Chun Liang Chun-Guo Wu College of Software Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Guangzhou Institute of China Telecom Guangzhou 510630 China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Key Laboratory of Information Science and Engineering of the Ministry of Railway/Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing Jiaotong University Beijing 100044 China.

Cheng and Church algorithm is an important approach in biclustering algorithms. In this paper, the process of the extended space in the second stage of Cheng and Church algorithm is improved and the selections of two important parameters are discussed. The results of the improved algorithm used in the gene expression spectrum analysis show that, compared with Cheng and Church algorithm, the quality of clustering results is enhanced obviously, the mining expression models are better, and the data possess a strong consistency with fluctuation on the condition while the computational time does not increase significantly.

关键词： biclustering algorithm gene expression pedigree analysis Cheng and Church algorithm

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An Enhanced Approach for Few-Shot Segmentation Via Smooth Downsampling Mask and Label Smoothing Loss

SSRN

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SSRN 2023年

作者： Jin, Hailong Li, Huiying College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China

Few-shot semantic segmentation aims to segment new categories with only a small number of annotated images. Previous methods mainly focused on exploiting the pixel-level correlation between the support image and the query image, combined with attention-based methods, resulting in significant advancements. In this paper, we introduce a new perspective to enhance few-shot segmentation. We identify that utilizing the bilinear interpolation method to downsample the mask leads to the loss of fine-grained information from the target features. To address this issue, we propose a Smooth Downsampling Mask (SDM) method. The SDM method is designed to retain more effective target semantic features by employing a cascaded downsampling approach with a smooth kernel for mask processing. Additionally, we propose a label smoothing loss to further enhance the performance, which provides direct guidance for low-resolution feature map optimization. Notably, our proposed method involves no additional learnable parameters and introduces only a small amount of extra computation. To validate the effectiveness of our approach, we conduct extensive experiments on three public benchmarks, PASCAL-$5^i$, COCO-$20^i$, and FSS-$1000$, and achieve considerable improvements. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Geometric constraint solving using PACAO

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Journal of computational Information Systems 2007年第2期3卷 423-428页

作者： Yuan, Hua Li, Wenhui Yi, Rongqin Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China School of Computer Science and Engineering Changchun University of Technology Changchun 130012 China

Previous methods of volume rendering are very slow and thus impractical. We present volume rendering based on marching cubes iso-surfacing and transfer function. For an iso-surface, we divide the voxels into logical cubes according to a predefined threshold value, the tangent planes and normal vectors at each voxel are calculated and the normal vectors are orientated to the outside of surface based on wide first searching (WFS), and the 3D surface model is then obtained using marching cubes. Transfer function is used to specify the optical properties for volume rendering technology. We employ a 2D function. The end-user interacts with a set of direct manipulation widgets (triangles and rectangles). Each widget precisely corresponds to a different material and widgets are blended automatically to compute an overall transfer function. Compared to traditional techniques, the overall specification process takes a fraction of the time.

关键词： Volume rendering

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Crossed-Time Delay Neural Network for Speaker Recognition

arXiv

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arXiv 2020年

作者： Chen, Liang Liang, Yanchun Shi, Xiaohu Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education School of Computer Zhuhai College Jilin University Zhuhai519041 China

Time Delay Neural Network (TDNN) is a well-performing structure for deep neural network-based speaker recognition systems. In this paper we introduce a novel structure, named Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN for speaker recognition. Inspired by the multi-filters setting of convolution layers from convolution neural networks, we set multiple time delay units with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition, CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, Factorized-TDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition. Moreover, the proposed CTDNN can handle training with a larger batch more efficiently and hence, utilize calculation resources economically. The code of the new model is released at https://***/chenllliang/CTDNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Robust and efficient annotation based on ontology evolution for deep web data

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Journal of Computers 2011年第10期6卷 2029-2036页

作者： Chen, Kerui Zuo, Wanli Zhang, Fan He, Fengling Chen, Yongheng Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China College of Information Engineering Zhongzhou University Zhengzhou 450044 China

Among those researches in Deep Web, compared to research of data extraction which is more mature, the research of data annotation is still at its preliminary stage. Currently, although the approach of applying ontology in data annotating has been approved by most researchers, there are many weaknesses existed, such as the complexity of the ontology, as well as the limitation on static ontology's ability to annotate new pages. Respond to those problems, this paper proposes a robust, highly efficient data annotation method based on ontology evolution. It needs to be noticed that this paper defines a simpler ontology which can improve annotating efficiency significantly. Experiments indicate that this method could improve the accuracy and efficiency of data annotation. © 2011 ACADEMY PUBLISHER.

关键词： Ontology

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Research and Development of Virtual Instruments System Based on Depth Camera

Research and Development of Virtual Instruments System Based...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Xiao-li Xu Ming-hui Sun Xin-yue Sun Wei-yu Zhao Xiaoying Sun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University College of Communication Engineering Jilin University

This paper takes advantage of the depth camera of somatosensory kinect and sensors to implement gesture recognition and design a virtual instrument system. As long as the user waves his arm without the help of other equipments, our system can automatically recognize the hand gesture and make suitable sound. In order to achieve depth camera's detection of hands movement, this paper introduce the depth imaging technology Light Coding and bone tracking technology to obtain the actual position information and hand movement information of the human body. Feet movement detection uses sensor technology, different stampede strength outputs different digital number after AD conversion so that the intensity can be controlled. A series of experiments show that the system has good fluency and practicality and increased the fun of playing instruments.

关键词： Virtual instrument Kinect Sensors Depth data processing Bone tracking

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Mutual conversion of regression and classification based on least squares support vector machines

Mutual conversion of regression and classification based on ...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Jiang, Jing-Qing Song, Chu-Yi Wu, Chun-Guo Liang, Yang-Chun Yang, Xiao-Wei Hao, Zhi-Feng Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China School of Mathematical Science South China University of Technology Guangzhou 510640 China

ISBN: (纸本)354034439X

Classification and regression are most interesting problems in the fields of pattern recognition. The regression problem can be changed into binary classification problem and least squares support vector machine can be used to solve the classification problem. The optimal hyperplane is the regression function. In this paper, a one-step method is presented to deal with the multi-category problem. The proposed method converts the problem of classification into the function regression problem and is applied to solve the converted problem by least squares support vector machines. The novel method classifies the samples in all categories simultaneously only by solving a set of linear equations. Demonstrations of numerical experiments are performed and good performances are obtained. Simulation results show that the regression and classification can be converted each other based on least squares support vector machines. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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A Sarsa() algorithm based on double-layer fuzzy reasoning

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Mathematical Problems in engineering 2013年 2013卷

作者： Liu, Quan Mu, Xiang Huang, Wei Fu, Qiming Zhang, Yonggang School of Computer Science and Technology Soochow University Suzhou Jiangsu 215006 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

Solving reinforcement learning problems in continuous space with function approximation is currently a research hotspot of machine learning. When dealing with the continuous space problems, the classic Q-iteration algorithms based on lookup table or function approximation converge slowly and are difficult to derive a continuous policy. To overcome the above weaknesses, we propose an algorithm named DFR-Sarsa() based on double-layer fuzzy reasoning and prove its convergence. In this algorithm, the first reasoning layer uses fuzzy sets of state to compute continuous actions;the second reasoning layer uses fuzzy sets of action to compute the components of Q-value. Then, these two fuzzy layers are combined to compute the Q-value function of continuous action space. Besides, this algorithm utilizes the membership degrees of activation rules in the two fuzzy reasoning layers to update the eligibility traces. Applying DFR-Sarsa() to the Mountain Car and Cart-pole Balancing problems, experimental results show that the algorithm not only can be used to get a continuous action policy, but also has a better convergence performance. © 2013 Quan Liu et al.

关键词： Fuzzy sets

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Improving the Recommendation of Collaborative Filtering by Fusing Trust Network

Improving the Recommendation of Collaborative Filtering by F...

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International Conference on computational Intelligence and Security

作者： Bo Yang Pengfei Zhao Shuqiu Ping Jing Huang School of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun P.R.China

To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely adopted recommender methods, whereas it is suffering the issue of sparse rating data that will severely degenerate the quality of recommendations. To address this issue, the article proposes a novel method, named the FTRA (Fusing Trust and Ratings), trying to improve the performance of collaborative filtering recommendation by means of elaborately integrating twofold sparse information, i.e., the conventional rating data given by users and the social trust network among the same users. The performance of FTRA is rigorously validated by comparing it with six representative methods on a real-world dataset. The experimental results show that the FTRA outperforms all other competitors in terms of both precision and recall. More importantly, our work suggests that the strategy of augmenting sparse rating data by fusing trust networks does significantly improve the quality of conventional collaborative filtering recommendation, and its quality could be further improved by means of designing more effective integrating schemes.

关键词： Collaboration Recommender systems Measurement Social network services Web sites Educational institutions

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