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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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CLASSIFICATION OF MASS SPECTROMETRY DATA Using Manifold and Supervised Distance Metric learning

CLASSIFICATION OF MASS SPECTROMETRY DATA <i>Using Manifold a...

引用

2nd international conference on Bio-Inspired Systems and signal processing

作者： Liu, Qingzhong Sung, Andrew H. Ribeiro, Bernardete M. Qiao, Mengyu Univ Coimbra Dept Informat Engn P-3000 Coimbra Portugal New Mexico Inst Min & Technol Inst Complex Addit Syst Anal Socorro NM 87801 USA Univ Coimbra Dept Informat Engn Coimbra Portugal

ISBN: (纸本)9789898111654

Mass spectrometry becomes the most widely used measurement in proteomics research. The quality of the feature set and applied learning classifier determine the reliability of the prediction of disease status. A well-known approach is to combine peak detection and support vector machine recursive feature elimination (SVMRFE). To compare the feature selection and to search for alternative learning classifier, in this paper, we employ a distance metric learning to classification of proteomics mass spectrometry (MS) data. Experimental results show that distance metric learning is promising for the classification of proteomics data;the results are comparable to the best results by applying SVM to the SVMRFE feature sets. Results also indicate that the good potential of manifold learning for feature reduction in MS data analysis.

关键词： Proteomics Mass spectrometry Manifold Distance metric learning Classification Support vector machine

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2nd IFAC international conference on Intelligent Control Systems and signal processing, ICONS 2009 - proceedings

2nd IFAC International Conference on Intelligent Control Sys...

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2nd IFAC international conference on Intelligent Control Systems and signal processing, ICONS 2009

ISBN: (纸本)9783902661661

The proceedings contain 99 papers. The topics discussed include: on the multivariable iterative learning control base on the gradient method;harmonic elimination control of an inverter based on an artificial neural network strategy;adaline-based approaches for time-varying frequency estimation in power systems;experimental modeling of propulsion transients of a brushless DC motor and propeller pair under limited power conditions: a neural network based approach;motion detection and tracking of classified objects with intelligent systems;real-time global optimization using multiple units;state estimation based optimal control and NARMA-L2 controllers of a scaled-model helicopter;constrained suboptimal dual control algorithms for discrete-time stochastic systems;the continuous system equivalent to the system with sliding mode control;and the air-jet texturing and twisting machine's and model predictive control based on state-space.

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The On-line Electronic Commerce Forecast Based on Least Square Support Vector machine

The On-line Electronic Commerce Forecast Based on Least Squa...

引用

2nd international conference on Information and Computing Science

作者： Zhou, Min Wang, Qiwan China Univ Min & Technol Sch Management Xuzhou 221008 Jiangsu Peoples R China

ISBN: (纸本)9780769536347

The using of least square support vector machine for on-line forecast has been gradually applied to the field on management science research. The traditional support vector machine algorithm contains inequality constraints, which requires solving quadratic programming problems so that the computing can be very complicated when there are a lot of training samples. In this paper, first of all, the least square support vector machine algorithm has been improved so as to solve the sparsity and time lag problems existing in traditional method, and then set up the LS-SVM on-line forecasting model of the least support value sample based on time factor eliminating, and input observed data on the network sale instances about one production into the model for testing. The results show that: the forecast and actual value are comfortably approximate, and can well indicate the trends of e-commerce sales forecast;the error between forecast and actual value from this method is smaller than the forecast error from common least square support vector machine method and BP neural network method.

关键词： least square support vector machine electronic commerce sales online learning forecast

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Towards Mutual Benefits E-training

Towards Mutual Benefits E-training

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2nd international conference on Information and Computing Science

作者： Yang, Zhanjiang Li, Yingjie Zhejiang Forestry Univ Personnel Dept Linan Zhejiang Peoples R China Zhejiang Forestry Univ Informat Engn Sch Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9780769536347

The main features and models of e-learning, e-training and e-commerce are discussed. C2C e-commerce model is introduced to e-training and a new e-training model is proposed. An e-training portal's frame relevant to the new model is depicted. The mutual benefit feature within the model is expected to improve the e-training efficiency continuously.

关键词： e-learning e-training e-commerce C2C model

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Solving the Steady Flight State of Aircraft Based on Hybrid Genetic Algorithm

Solving the Steady Flight State of Aircraft Based on Hybrid ...

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2nd international conference on Information and Computing Science

作者： Luan Zhibo Huang Qitao Jiang Hongzhou Li Hongren Harbin Inst Technol Sch Mechatron Engn Harbin 150006 Peoples R China

ISBN: (纸本)9780769536347

Steady flat flight is widely used in the flight simulator training as an ideal initial state. To ensure the accurate solving of the steady flat flight state a hybrid genetic algorithm is put forward. The algorithm based on the new concept of "individual learning potentiality" make the Lamarckian learning and Baldwinina learning genetic algorithm combination together organically according to the particularity of the solving in the steady flat flight state. The algorithm could make the advantage of the learning into full play and make the disadvantage into inhibitory. The algorithm has generality which just use the state variable to calculate and can be independent of the airplane dynamic. Simulation result shows that the new algorithm combined the tow learning mechanism has made a good effect.

关键词： Genetic algorithm Steady flight state Individual learning potentiality Lamarckian learning Baldwinina learning

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Realistic measurement of student attendance in LMS using biometrics

Realistic measurement of student attendance in LMS using bio...

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2nd international Multi-conference on Engineering and Technological Innovation, IMETI 2009

作者： González-Agulla, Elisardo Alba-Castro, Jose L. Argones-Rúa, Enrique Anido-Rifón, Luis Signal Processing and Communications Department University of Vigo Spain Telematics Engineering Department University of Vigo Spain

ISBN: (纸本)1934272698

In this paper we propose a solution to obtain useful and reliable student session logs in a learning Management System (LMS) combining current logs with biometrics-based logs that show the student behaviour during the whole learning session. The aims of our solution are to guarantee that the online student is who he/she claims to be, and also to know exactly how much time he/she spends in front of the computer reading each LMS content. Even when the proposed solution does not completely avoid cheating, the use of biometrie data during authentication and face tracking provides additional help to validate student performance during learning sessions. In this way it is possible to improve security for specific contents, to gain feedback of the student effort and to check the actual time spent in learning.

关键词： Biometrics

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learning a Locality Preserving subspace for ISAR Target Recognition

Learning a Locality Preserving subspace for ISAR Target Reco...

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2nd international conference on Information and Computing Science

作者： Cai, Hong He, Qiang Han, Zhuang-Zhi Shang, Chao-Xuan Ordnance Engn Coll Dept Opt & Elect Engn Shijiazhuang Peoples R China

ISBN: (纸本)9780769536347

Io order to overcome the instability of Inverse synthetic aperture radar (ISAR) image caused by shift, rotation and scale variation, a new approach based on Locality Preserving Projections (LPP) algorithm of manifold learning is proposed to feature analysis in ISAR target recognition. Firstly, the LPP algorithm is used to reduce the dimensionality of the ISAR image, and then the reduced feature is classified by k-nearest neighbor classification with rejection recognition capability. Experimental results on four kinds of aircraft target suggest that the LPP algorithm has the capability of finding the low-dimensional manifold structure embedded in the high-dimensional ISAR image space, which is controlled by few parameters, such as attitude angle, scale and position, etc., and the better classification performance is acquired with the low-dimensional feature.

关键词： manifold learning locality preserving projections ISAR image target recognition

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ASSESSMENT And COMPARISON OF TIME REALIGNMENT METHODS FOR SUPERVISED HEART BEAT CLASSIFICATION

ASSESSMENT AND COMPARISON OF TIME REALIGNMENT METHODS FOR SU...

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2nd international conference on Bio-Inspired Systems and signal processing

作者： de Lannoy, G. Verleysen, M. Delbeke, J. Catholic Univ Louvain Machine Learning Grp Pl Levant 3 B-1348 Louvain La Neuve Belgium Catholic Univ Louvain Dept Physiol & Pharmacol B-1200 Brussels Belgium

ISBN: (纸本)9789898111654

A reliable diagnosis of cardiac diseases can sometimes only be obtained by observing the heart of a patient for a long time period where every single heart beat is of importance. Computer-aided classification of heart beats is therefore of great help. The classification of the complete heart beat has many advantages compared to a classification of the QRS complex only or feature extraction methods. Nevertheless, the task is challenging because of the time-varying property of the heart beats. In this work, four time-alignment methods are evaluated and compared in the context of supervised heart beat classification. Among the four methods are three time series resampling methods by linear interpolation, cubic splines interpolation and trace segmentation. The fourth method is a realignment algorithm by dynamic time warping. The multiple sources of artifacts are filtered by discrete wavelet transform. As it only relies on a dissimilarity measure, the k-nearest neighbor classifier is a suitable choice for supervised classification of time series like ECG signals in multiple classes. Two different experiments corresponding to inter-patient and intra-patient classification are conducted on representative dataset built from the standard public MIT-BIH arrhythmia database.

关键词： Heart beat classification Time realignment Dynamic time warping Trace segmentation Wavelet transform Nearest neighbor classifier

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Phonetic classification with spiking neural network using a gradient descent rule

Phonetic classification with spiking neural network using a ...

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2nd international conference on Computer and Electrical Engineering

作者： Ourdighi, A. Lacheheb, S. E. Benyettou, A. Mohamed Boudiaf Sci & Technol Univ Fac Sci Dept Comp Sci Signal Image Speech Lab SIMPA Oran Algeria

ISBN: (纸本)9780769539256

Being the closest model of the biological neuron, the spiking neuron is the third and newest generation of artificial neuron. the particularity of this neuron is the use of temporal coding to pass information between network units. Using such codes allows the transmission of a large amount of data with only few spikes, simply one or zero for each neuron involved in the specific processing task. the true deal is how to encode analogical information to a spikes train. More, it's not the only problem which we find in using spiking neurons network (SNN), we have to choose different parameters and functions. In this paper, in the middle of several spiking neurons models, we have chosen the spiking response model (SRM) to apply in phonetic classification using phonemes from TIMIT databases. Before, for the studies, we have performed experiments for the classical Xor-problem and explore the impact of encoding information on the network structure. The learning rules used in this experiment was based on error backpropagation based on time to first spike.

关键词： SNN Spike Integrate and fire time to first spike

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Evaluation on competitiveness of TPL enterprises based on AHP & genetic algorithm

Evaluation on competitiveness of TPL enterprises based on AH...

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international conference on Computer Science and Information Technology (CSIT)

作者： Yan Ge School of Business Administration Shandong University of Finance Ji'nan 250014 China

Third party logistics is the advanced form of modern logistics for socialization and specialization. The evaluation optical selection of third-party logistics enterprises increasingly becomes the key points of the recycling economy and green environmental protection industry. Analytical hierarchy process (AHP), a commonly used quantitative research method, is the widely used evaluation indicator solution. The genetic algorithm which is one of the major technologies of intelligent calculation, with adaptively dynamic adjustment and global optimization capability, has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life. Based on the characteristics of the two methods, the paper designs a program making real-time feedback of information according to AHP, adopting genetic algorithm combined with AHP dynamic adjusting third party logistics enterprises competitiveness evaluation index weights, to further enhance the objectivity and efficiency of the system evaluation of third party logistics enterprises.

关键词： Genetic algorithms Logistics Optical feedback Optical signal processing Recycling Protection Artificial intelligence learning systems machine learning signal processing algorithms

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