Parallel Data Selection Based on Neurodynamic Optimization in the Era of Big Data
|
|
- Arthur Burns
- 8 years ago
- Views:
Transcription
1 Parallel Data Selection Based on Neurodynamic Optimization in the Era of Big Data Jun Wang Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin, New Territories, Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian, Liaoning, China
2 Outline Introduction Problem formulations kwta networks Simulation results Sorting application Filtering Application Concluding remarks Future works References
3 Multiple Winners-take-all Operation The k-winners-take-all (kwta) operation is to select the k largest inputs out of n inputs (1 k < n). kwta is a general rule in nature and society. kwta has widespread applications in data mining, machine learning, classification, clustering, computer vision, etc. It is a common building block for many models such as ART and SOM.
4 k Winners-take-all Operation As the number of inputs increases and/or the selection process should be operated in real time, parallel algorithms and hardware implementation are desirable.
5 Parallel k Winners-take-all Operation x 1 x 2 x n k u 1 u 2 u n
6 Problem Formulations "The mere formulation of a problem is far more essential than its solution, which may be merely a matter of mathematical or experimental skills. To raise new questions, new possibilities, to regard old problems from a new angle requires creative imagination and marks real advances in science." Albert Einstein
7 Problem Formulations
8 Problem Formulations (cont d)
9 Problem Formulations (cont d)
10 Problem Formulations (cont d)
11 Model Selection and Redesign The ktwa problem has been formulated as an equivalent linear and quadratic programming problems. All existing neurodynamic optimization models for linear and quadratic programming can be applied. Now the question is: which is the best in terms of model complexity and computational efficiency?
12 QP-based Primal-Dual Network
13 QP-based Projection Network
14 LP-based Projection Network
15 QP-based Simplified Dual Net
16 LP-based Discontinuous Network
17 Discontinuous Activation Function
18 Convergence Conditions
19 Simulation Results
20 Simulation Results (cont d)
21 Simulation Results (cont d)
22 QP-based Discontinuous Network
23 Discontinuous Activation Function
24 Convergence Condition
25 Simulation Results
26 Simulation Results(cont d)
27 Simulation Results (cont d)
28 QP-based Improved Dual Network
29 Model Comparisons Model Number of layer(s) Number of neuron(s) Number of connections LP-based primal-dual network QP-based primal-dual network 4 3n + 1 6n n + 1 6n + 2 LP-based projection network 2 n + 1 2n + 2 QP-based projection network QP-based simplified dual network 2 n + 1 2n n 3n LP-based discontinuous net 1 n 2n QP-based discontinuous network QP-based improved dual network 1 n 2n 1 1 n
30 Simulation Results
31 Discrete-time Counterpart
32 Activation Function with High Gain
33 A New Model
34 Desirable Properties The kwta model with Heaviside activation function has been proven to be globally stable and globally convergent to the kwta solutions in finite time. Derived lower and upper bounds of convergence time are respectively It essentially solves the dual problem of the linear programming formulation.
35 Convergence Time As a linear system with a discontinuous bias, the converence time of the kwta network can be computed as a function of input vector u. The expectation and variance of the convergence time can also be computed, based on Binomial distribution, as functions of initial states. Y. Xiao, Y. Liu, C.-S. Leung, J. P.-F. Sum, K. Ho, Analysis on the convergence time of dual neural network-based kwta, IEEE Trans. Neural Networks and Learning Systems, vol. 23, pp , J. P.-F. Sum, C.-S. Leung, K. Ho, Effect of Input Noise and Output Node Stochastic on Wang's kwta, IEEE Trans. Neural Networks and Learning Systems, vol. 24, pp , 2013.
36 Reformulated Problem
37 Reformulated Problem (cont d)
38 Reformulated Problem (cont d)
39 Simulation Results with Randomized Integer Inputs
40 Simulation Results with Low- Resolution Inputs
41 Initial State Estimation Although the state of kwta model is guaranteed to be globally convergent in finite time from any initial state, prior information is helpful to initialize the state closely to the steady state. Obviously, the steady state of y (u k+1, u k ] depends on the distribution of u 1, u 2,..., u n, as well as the values of k and n.
42 Initial State Estimation (cont d) General distribution Uniform distribution Normal distribution
43 Initial State Estimation (cont d)
44 Uniform Distribution
45 Normal Distribution
46 Simulation Results (convergence time) with Infinity Gain
47 Simulation Results (convergence time) with Unity Gain
48 Discrete-time Version
49 Simulation Results (n = 10 6, k = n/2)
50 Simulation Results (n = 10 6, k = n/2)
51 Monte Carlo Simulation Results
52 Monte Carlo Simulation Results
53 Estimated Complexity (uniform)
54 Estimated Complexity (normal) For data with a dimension of (1 Googol), it would need about 8.44 iterations on average!
55 Histograms of Convergence Iterations
56 Histograms of Convergence Iterations
57 Histograms of Convergence Iterations
58 Histograms of Convergence Iterations
59 Sorting Operation Sorting is a fundamental process to arrange data in an order according to their values. It accounts for 25% of data processing time (Knuth). For sorting with large number or high dimensional data, parallel sorting approaches are more desirable. Numerous sorting algorithms and models have been developed with varied efficiencies.
60 Parallel Sorting Representation For example, a permutation matrix:
61 Parallel Sorting Representation (cont d) A modified version:
62 Logic Reversal A simple logic can be used to flip over the redundant '1' elements after the first '1' in each row; i.e.,
63 Parallel Sorting based on kwta Let each kwta network computes one column of the above sorting matrix from left to right with k increasing from 1 to n - 1. Specifically, a WTA network with a single state variable (i.e., k=1) is adopted to determined the largest element of the list. Next, a kwta network with k = 2 computes the second item in the list without recounting the first item.
64 Parallel Sorting based on kwta As such, the whole list of n items can be sorted using n-1 kwta networks without the need for computing the last item. As a result, only n-1 neurons will be needed. It is a substantial reduction of the model complexity compared with the analog sorting networks with n 2 neurons.
65 Illustrative Example In this case, only five (5) neurons are needed by using five kwta networks here. In contrast, 36 neurons are needed in the analog sorting network (Wang, 1995).
66 Simulation Results (state variable)
67 Simulation Results (output variables)
68 Rank-order Filter Rank order filters are nonlinear filters with many applications including digital image processing, speech processing, coding and digital TV, etc. A rank order filter functions by working by selecting its input with a certain rank as its output. Rank order filters entails substantial processing power to implement, which limits their real-time signal processing applications.
69 Rank-order Filter Based on kwta Nevertheless, rank order filters can benefit from their parallelism realizations. Specifically, a kwta network with k = r is used in parallel to another kwta network with k = r 1 to select the input with its rank order being r.
70 Simulation Results (median filter)
71 Simulation Results (median filter)
72 Simulation Results (median filter)
73 Image Processing Percentage of speckle noise in image 10%
74 Image Filtering (cont d)
75 Image Filtering (cont d)
76 Image Filtering (cont d)
77 Image Filtering (cont d)
78 Image Filtering (cont d) Put the original image into median filter The Original image Original image after median filtering
79 Color Image Filtering Percentage of speckle noise in image 10%
80 Color Image Filtering (cont d) Percentage of speckle noise in image 10%
81 Color Image Filtering (cont d)
82 Color Image Filtering (cont d)
83 Color Image Filtering (cont d)
84 Results & Discussion - Image Processing
85 Color Image Filtering (cont d)
86 Color Image Filtering (cont d)
87 Information Retrieval The efficiency of information retrieval from large database is essential. The techniques for information retrieval from large data sets play a very important role as the size of the world-wide web exceeded possibly more than 30 billion nowadays.
88 Web Information Retrieval There are basically two parts in web information retrieval: One is calculating the weight of all the pages or data. The other is find the most wanted k results with highest weightings. The second one is the top-k query or front page problem.
89 A Toy Problem from Wikipedia 7 pages 17 links The PageRank weight of each page and link is provided.
90 Selection Results (k=3) Output vector x=[1,1,0,0,1,0,0] T Pages 1, 2, and 5 are with higher PageRank weights
91 Film-director-actor-writer Network Crawled from Wikipedia under the category of English language films 34,279 pages 142,426 links Part of the square adjacency matrix is shown by the figure, where a dot on the i th column and the j th row represents that there is a directed link pointed to the j th page from the i th one. The rest of the matrix is 0.
92 Selection Results (k=10) The answer to this query [3111, 3869, 4058, 4621, 6938, 8974, 10341, 11502, 13320, 15326] T can be easily achieved from the sparse representation of the output vector x = g(u i -y(t)), where 10 of the elements are nonzero.
93 Conclusions and Future Works The neurodynamic optimization approaches are demonstrated to be powerful for k-winners-take-all operations. k-winners-take-all neural networks provide parallel distributed computational models with guaranteed global convergence to the optimal solutions. Neurodynamic optimization approaches are more suitable for real-time applications with big data. GPU-based implementation is under way. Applications to other problems such as recommender systems are yet to be done.
94 Acknowledgments Prof. Yousheng Xia (Fuzhou University) Prof. Yunong Zhang (Sun Yat-sen University) Prof. Xiaolin Hu (Tsinghua University) Prof. Qingshan Liu (Huazhong Univ. of Sci. and Tech.) Dr. Shubao Liu (GE Global Research) Dr. Zheng Yan (Huawei Shannon Laboratory) Mr. Yunpeng Pan (Georgia Institute of Technology) Mr. Zhishan Guo (University of North Carolina) Mr. Shaofu Yang and Miss Xinyi Le (Chinese University of Hong Kong) Many projects funded by the Hong Kong Research Grants Council.
95 Q & A
Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks
Chin. J. Astron. Astrophys. Vol. 5 (2005), No. 2, 203 210 (http:/www.chjaa.org) Chinese Journal of Astronomy and Astrophysics Automated Stellar Classification for Large Surveys with EKF and RBF Neural
More informationAnalecta Vol. 8, No. 2 ISSN 2064-7964
EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,
More informationLarge-scale Data Mining: MapReduce and Beyond Part 2: Algorithms. Spiros Papadimitriou, IBM Research Jimeng Sun, IBM Research Rong Yan, Facebook
Large-scale Data Mining: MapReduce and Beyond Part 2: Algorithms Spiros Papadimitriou, IBM Research Jimeng Sun, IBM Research Rong Yan, Facebook Part 2:Mining using MapReduce Mining algorithms using MapReduce
More informationSupport Vector Machines with Clustering for Training with Very Large Datasets
Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano
More informationModelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches
Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic
More informationFault Analysis in Software with the Data Interaction of Classes
, pp.189-196 http://dx.doi.org/10.14257/ijsia.2015.9.9.17 Fault Analysis in Software with the Data Interaction of Classes Yan Xiaobo 1 and Wang Yichen 2 1 Science & Technology on Reliability & Environmental
More informationLoad balancing in a heterogeneous computer system by self-organizing Kohonen network
Bull. Nov. Comp. Center, Comp. Science, 25 (2006), 69 74 c 2006 NCC Publisher Load balancing in a heterogeneous computer system by self-organizing Kohonen network Mikhail S. Tarkov, Yakov S. Bezrukov Abstract.
More informationFigure 1. The cloud scales: Amazon EC2 growth [2].
- Chung-Cheng Li and Kuochen Wang Department of Computer Science National Chiao Tung University Hsinchu, Taiwan 300 shinji10343@hotmail.com, kwang@cs.nctu.edu.tw Abstract One of the most important issues
More informationLarge-Scale Data Sets Clustering Based on MapReduce and Hadoop
Journal of Computational Information Systems 7: 16 (2011) 5956-5963 Available at http://www.jofcis.com Large-Scale Data Sets Clustering Based on MapReduce and Hadoop Ping ZHOU, Jingsheng LEI, Wenjun YE
More informationText Mining Approach for Big Data Analysis Using Clustering and Classification Methodologies
Text Mining Approach for Big Data Analysis Using Clustering and Classification Methodologies Somesh S Chavadi 1, Dr. Asha T 2 1 PG Student, 2 Professor, Department of Computer Science and Engineering,
More informationOpen Access Research on Application of Neural Network in Computer Network Security Evaluation. Shujuan Jin *
Send Orders for Reprints to reprints@benthamscience.ae 766 The Open Electrical & Electronic Engineering Journal, 2014, 8, 766-771 Open Access Research on Application of Neural Network in Computer Network
More informationIntroduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk
Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trakovski trakovski@nyus.edu.mk Neural Networks 2 Neural Networks Analogy to biological neural systems, the most robust learning systems
More informationMean-Shift Tracking with Random Sampling
1 Mean-Shift Tracking with Random Sampling Alex Po Leung, Shaogang Gong Department of Computer Science Queen Mary, University of London, London, E1 4NS Abstract In this work, boosting the efficiency of
More informationThe multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2
2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) The multilayer sentiment analysis model based on Random forest Wei Liu1, Jie Zhang2 1 School of
More informationChapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network
Chapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network Qian Wu, Yahui Wang, Long Zhang and Li Shen Abstract Building electrical system fault diagnosis is the
More informationPerformance Evaluation and Prediction of IT-Outsourcing Service Supply Chain based on Improved SCOR Model
Performance Evaluation and Prediction of IT-Outsourcing Service Supply Chain based on Improved SCOR Model 1, 2 1 International School of Software, Wuhan University, Wuhan, China *2 School of Information
More informationThe Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
More informationSECRET sharing schemes were introduced by Blakley [5]
206 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 1, JANUARY 2006 Secret Sharing Schemes From Three Classes of Linear Codes Jin Yuan Cunsheng Ding, Senior Member, IEEE Abstract Secret sharing has
More informationA Simple Feature Extraction Technique of a Pattern By Hopfield Network
A Simple Feature Extraction Technique of a Pattern By Hopfield Network A.Nag!, S. Biswas *, D. Sarkar *, P.P. Sarkar *, B. Gupta **! Academy of Technology, Hoogly - 722 *USIC, University of Kalyani, Kalyani
More informationHow To Get A Computer Science Degree At Appalachian State
118 Master of Science in Computer Science Department of Computer Science College of Arts and Sciences James T. Wilkes, Chair and Professor Ph.D., Duke University WilkesJT@appstate.edu http://www.cs.appstate.edu/
More informationResearch on Semantic Web Service Composition Based on Binary Tree
, pp.133-142 http://dx.doi.org/10.14257/ijgdc.2015.8.2.13 Research on Semantic Web Service Composition Based on Binary Tree Shengli Mao, Hui Zang and Bo Ni Computer School, Hubei Polytechnic University,
More informationEFFICIENT DATA PRE-PROCESSING FOR DATA MINING
EFFICIENT DATA PRE-PROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College
More informationNEUROMATHEMATICS: DEVELOPMENT TENDENCIES. 1. Which tasks are adequate of neurocomputers?
Appl. Comput. Math. 2 (2003), no. 1, pp. 57-64 NEUROMATHEMATICS: DEVELOPMENT TENDENCIES GALUSHKIN A.I., KOROBKOVA. S.V., KAZANTSEV P.A. Abstract. This article is the summary of a set of Russian scientists
More informationHow To Fix Out Of Focus And Blur Images With A Dynamic Template Matching Algorithm
IJSTE - International Journal of Science Technology & Engineering Volume 1 Issue 10 April 2015 ISSN (online): 2349-784X Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode
More informationA Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster
, pp.11-20 http://dx.doi.org/10.14257/ ijgdc.2014.7.2.02 A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster Kehe Wu 1, Long Chen 2, Shichao Ye 2 and Yi Li 2 1 Beijing
More informationDistance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center
Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center 1 Outline Part I - Applications Motivation and Introduction Patient similarity application Part II
More informationBlog Post Extraction Using Title Finding
Blog Post Extraction Using Title Finding Linhai Song 1, 2, Xueqi Cheng 1, Yan Guo 1, Bo Wu 1, 2, Yu Wang 1, 2 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 2 Graduate School
More informationFunctional-Repair-by-Transfer Regenerating Codes
Functional-Repair-by-Transfer Regenerating Codes Kenneth W Shum and Yuchong Hu Abstract In a distributed storage system a data file is distributed to several storage nodes such that the original file can
More informationForecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network
Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network Dušan Marček 1 Abstract Most models for the time series of stock prices have centered on autoregressive (AR)
More informationThe Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network
, pp.67-76 http://dx.doi.org/10.14257/ijdta.2016.9.1.06 The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network Lihua Yang and Baolin Li* School of Economics and
More informationA Network Simulation Experiment of WAN Based on OPNET
A Network Simulation Experiment of WAN Based on OPNET 1 Yao Lin, 2 Zhang Bo, 3 Liu Puyu 1, Modern Education Technology Center, Liaoning Medical University, Jinzhou, Liaoning, China,yaolin111@sina.com *2
More informationCS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing
CS Master Level Courses and Areas The graduate courses offered may change over time, in response to new developments in computer science and the interests of faculty and students; the list of graduate
More informationNew Ensemble Combination Scheme
New Ensemble Combination Scheme Namhyoung Kim, Youngdoo Son, and Jaewook Lee, Member, IEEE Abstract Recently many statistical learning techniques are successfully developed and used in several areas However,
More informationK-Means Clustering Tutorial
K-Means Clustering Tutorial By Kardi Teknomo,PhD Preferable reference for this tutorial is Teknomo, Kardi. K-Means Clustering Tutorials. http:\\people.revoledu.com\kardi\ tutorial\kmean\ Last Update: July
More informationTHE Walsh Hadamard transform (WHT) and discrete
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I: REGULAR PAPERS, VOL. 54, NO. 12, DECEMBER 2007 2741 Fast Block Center Weighted Hadamard Transform Moon Ho Lee, Senior Member, IEEE, Xiao-Dong Zhang Abstract
More informationLoad Balancing and Switch Scheduling
EE384Y Project Final Report Load Balancing and Switch Scheduling Xiangheng Liu Department of Electrical Engineering Stanford University, Stanford CA 94305 Email: liuxh@systems.stanford.edu Abstract Load
More informationSteven C.H. Hoi. School of Computer Engineering Nanyang Technological University Singapore
Steven C.H. Hoi School of Computer Engineering Nanyang Technological University Singapore Acknowledgments: Peilin Zhao, Jialei Wang, Hao Xia, Jing Lu, Rong Jin, Pengcheng Wu, Dayong Wang, etc. 2 Agenda
More informationGraph Processing and Social Networks
Graph Processing and Social Networks Presented by Shu Jiayu, Yang Ji Department of Computer Science and Engineering The Hong Kong University of Science and Technology 2015/4/20 1 Outline Background Graph
More informationComponent Ordering in Independent Component Analysis Based on Data Power
Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals
More information350 Serra Mall, Stanford, CA 94305-9515
Meisam Razaviyayn Contact Information Room 260, Packard Building 350 Serra Mall, Stanford, CA 94305-9515 E-mail: meisamr@stanford.edu Research Interests Education Appointments Large scale data driven optimization
More informationFalloc: Fair Network Bandwidth Allocation in IaaS Datacenters via a Bargaining Game Approach
Falloc: Fair Network Bandwidth Allocation in IaaS Datacenters via a Bargaining Game Approach Fangming Liu 1,2 In collaboration with Jian Guo 1,2, Haowen Tang 1,2, Yingnan Lian 1,2, Hai Jin 2 and John C.S.
More informationKnowledge Discovery and Data Mining. Structured vs. Non-Structured Data
Knowledge Discovery and Data Mining Unit # 2 1 Structured vs. Non-Structured Data Most business databases contain structured data consisting of well-defined fields with numeric or alphanumeric values.
More informationPATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical
More informationAssessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall
Automatic Photo Quality Assessment Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Estimating i the photorealism of images: Distinguishing i i paintings from photographs h Florin
More informationStability of the LMS Adaptive Filter by Means of a State Equation
Stability of the LMS Adaptive Filter by Means of a State Equation Vítor H. Nascimento and Ali H. Sayed Electrical Engineering Department University of California Los Angeles, CA 90095 Abstract This work
More informationMethodology for Emulating Self Organizing Maps for Visualization of Large Datasets
Methodology for Emulating Self Organizing Maps for Visualization of Large Datasets Macario O. Cordel II and Arnulfo P. Azcarraga College of Computer Studies *Corresponding Author: macario.cordel@dlsu.edu.ph
More informationComputational Neural Network for Global Stock Indexes Prediction
Computational Neural Network for Global Stock Indexes Prediction Dr. Wilton.W.T. Fok, IAENG, Vincent.W.L. Tam, Hon Ng Abstract - In this paper, computational data mining methodology was used to predict
More informationRESEARCH INTERESTS Modeling and Simulation, Complex Systems, Biofabrication, Bioinformatics
FENG GU Assistant Professor of Computer Science College of Staten Island, City University of New York 2800 Victory Boulevard, Staten Island, NY 10314 Doctoral Faculty of Computer Science Graduate Center
More informationFUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM
International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 34-48 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT
More informationIMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS
IMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS Liuxi Zhang and Ali Abur Department of Electrical and Computer Engineering Northeastern University Boston, MA, USA lzhang@ece.neu.edu
More informationData Storage 3.1. Foundations of Computer Science Cengage Learning
3 Data Storage 3.1 Foundations of Computer Science Cengage Learning Objectives After studying this chapter, the student should be able to: List five different data types used in a computer. Describe how
More informationMulti-layer Structure of Data Center Based on Steiner Triple System
Journal of Computational Information Systems 9: 11 (2013) 4371 4378 Available at http://www.jofcis.com Multi-layer Structure of Data Center Based on Steiner Triple System Jianfei ZHANG 1, Zhiyi FANG 1,
More informationA linear algebraic method for pricing temporary life annuities
A linear algebraic method for pricing temporary life annuities P. Date (joint work with R. Mamon, L. Jalen and I.C. Wang) Department of Mathematical Sciences, Brunel University, London Outline Introduction
More informationDetection and Restoration of Vertical Non-linear Scratches in Digitized Film Sequences
Detection and Restoration of Vertical Non-linear Scratches in Digitized Film Sequences Byoung-moon You 1, Kyung-tack Jung 2, Sang-kook Kim 2, and Doo-sung Hwang 3 1 L&Y Vision Technologies, Inc., Daejeon,
More informationImage Compression through DCT and Huffman Coding Technique
International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul
More informationNon-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning
Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step
More informationSYMMETRIC EIGENFACES MILI I. SHAH
SYMMETRIC EIGENFACES MILI I. SHAH Abstract. Over the years, mathematicians and computer scientists have produced an extensive body of work in the area of facial analysis. Several facial analysis algorithms
More informationBernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA
Are Image Quality Metrics Adequate to Evaluate the Quality of Geometric Objects? Bernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA ABSTRACT
More informationResearch of Postal Data mining system based on big data
3rd International Conference on Mechatronics, Robotics and Automation (ICMRA 2015) Research of Postal Data mining system based on big data Xia Hu 1, Yanfeng Jin 1, Fan Wang 1 1 Shi Jiazhuang Post & Telecommunication
More informationIntrusion Detection via Machine Learning for SCADA System Protection
Intrusion Detection via Machine Learning for SCADA System Protection S.L.P. Yasakethu Department of Computing, University of Surrey, Guildford, GU2 7XH, UK. s.l.yasakethu@surrey.ac.uk J. Jiang Department
More informationNetwork Traffic Prediction Based on the Wavelet Analysis and Hopfield Neural Network
Netork Traffic Prediction Based on the Wavelet Analysis and Hopfield Neural Netork Sun Guang Abstract Build a mathematical model is the key problem of netork traffic prediction. Traditional single netork
More informationAn Imbalanced Spam Mail Filtering Method
, pp. 119-126 http://dx.doi.org/10.14257/ijmue.2015.10.3.12 An Imbalanced Spam Mail Filtering Method Zhiqiang Ma, Rui Yan, Donghong Yuan and Limin Liu (College of Information Engineering, Inner Mongolia
More informationStudy on the Evaluation for the Knowledge Sharing Efficiency of the Knowledge Service Network System in Agile Supply Chain
Send Orders for Reprints to reprints@benthamscience.ae 384 The Open Cybernetics & Systemics Journal, 2015, 9, 384-389 Open Access Study on the Evaluation for the Knowledge Sharing Efficiency of the Knowledge
More informationOptimization Modeling for Mining Engineers
Optimization Modeling for Mining Engineers Alexandra M. Newman Division of Economics and Business Slide 1 Colorado School of Mines Seminar Outline Linear Programming Integer Linear Programming Slide 2
More informationHow To Understand The Theory Of Probability
Graduate Programs in Statistics Course Titles STAT 100 CALCULUS AND MATR IX ALGEBRA FOR STATISTICS. Differential and integral calculus; infinite series; matrix algebra STAT 195 INTRODUCTION TO MATHEMATICAL
More informationA Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks
A Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks H. T. Kung Dario Vlah {htk, dario}@eecs.harvard.edu Harvard School of Engineering and Applied Sciences
More informationNeural Networks in Data Mining
IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 03 (March. 2014), V6 PP 01-06 www.iosrjen.org Neural Networks in Data Mining Ripundeep Singh Gill, Ashima Department
More informationNeural Network based Vehicle Classification for Intelligent Traffic Control
Neural Network based Vehicle Classification for Intelligent Traffic Control Saeid Fazli 1, Shahram Mohammadi 2, Morteza Rahmani 3 1,2,3 Electrical Engineering Department, Zanjan University, Zanjan, IRAN
More informationThe PageRank Citation Ranking: Bring Order to the Web
The PageRank Citation Ranking: Bring Order to the Web presented by: Xiaoxi Pang 25.Nov 2010 1 / 20 Outline Introduction A ranking for every page on the Web Implementation Convergence Properties Personalized
More informationDesign call center management system of e-commerce based on BP neural network and multifractal
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(6):951-956 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Design call center management system of e-commerce
More informationNonlinear Iterative Partial Least Squares Method
Numerical Methods for Determining Principal Component Analysis Abstract Factors Béchu, S., Richard-Plouet, M., Fernandez, V., Walton, J., and Fairley, N. (2016) Developments in numerical treatments for
More informationEstimating PageRank Values of Wikipedia Articles using MapReduce
Estimating PageRank Values of Wikipedia Articles using MapReduce Due: Sept. 30 Wednesday 5:00PM Submission: via Canvas, individual submission Instructor: Sangmi Pallickara Web page: http://www.cs.colostate.edu/~cs535/assignments.html
More informationCombating Anti-forensics of Jpeg Compression
IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 3, November 212 ISSN (Online): 1694-814 www.ijcsi.org 454 Combating Anti-forensics of Jpeg Compression Zhenxing Qian 1, Xinpeng
More informationThe Mathematics Behind Google s PageRank
The Mathematics Behind Google s PageRank Ilse Ipsen Department of Mathematics North Carolina State University Raleigh, USA Joint work with Rebecca Wills Man p.1 Two Factors Determine where Google displays
More informationComparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations
Volume 3, No. 8, August 2012 Journal of Global Research in Computer Science REVIEW ARTICLE Available Online at www.jgrcs.info Comparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations
More informationComparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data
CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear
More informationPredict Influencers in the Social Network
Predict Influencers in the Social Network Ruishan Liu, Yang Zhao and Liuyu Zhou Email: rliu2, yzhao2, lyzhou@stanford.edu Department of Electrical Engineering, Stanford University Abstract Given two persons
More informationAPPM4720/5720: Fast algorithms for big data. Gunnar Martinsson The University of Colorado at Boulder
APPM4720/5720: Fast algorithms for big data Gunnar Martinsson The University of Colorado at Boulder Course objectives: The purpose of this course is to teach efficient algorithms for processing very large
More informationArtificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence
Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support
More informationData Mining Applications in Fund Raising
Data Mining Applications in Fund Raising Nafisseh Heiat Data mining tools make it possible to apply mathematical models to the historical data to manipulate and discover new information. In this study,
More informationRecurrent Neural Networks
Recurrent Neural Networks Neural Computation : Lecture 12 John A. Bullinaria, 2015 1. Recurrent Neural Network Architectures 2. State Space Models and Dynamical Systems 3. Backpropagation Through Time
More informationSupply Chain Forecasting Model Using Computational Intelligence Techniques
CMU.J.Nat.Sci Special Issue on Manufacturing Technology (2011) Vol.10(1) 19 Supply Chain Forecasting Model Using Computational Intelligence Techniques Wimalin S. Laosiritaworn Department of Industrial
More informationEnsemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05
Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification
More informationGTC 2014 San Jose, California
GTC 2014 San Jose, California An Approach to Parallel Processing of Big Data in Finance for Alpha Generation and Risk Management Yigal Jhirad and Blay Tarnoff March 26, 2014 GTC 2014: Table of Contents
More informationMachine Learning. CUNY Graduate Center, Spring 2013. Professor Liang Huang. huang@cs.qc.cuny.edu
Machine Learning CUNY Graduate Center, Spring 2013 Professor Liang Huang huang@cs.qc.cuny.edu http://acl.cs.qc.edu/~lhuang/teaching/machine-learning Logistics Lectures M 9:30-11:30 am Room 4419 Personnel
More informationRandom forest algorithm in big data environment
Random forest algorithm in big data environment Yingchun Liu * School of Economics and Management, Beihang University, Beijing 100191, China Received 1 September 2014, www.cmnt.lv Abstract Random forest
More informationA Health Degree Evaluation Algorithm for Equipment Based on Fuzzy Sets and the Improved SVM
Journal of Computational Information Systems 10: 17 (2014) 7629 7635 Available at http://www.jofcis.com A Health Degree Evaluation Algorithm for Equipment Based on Fuzzy Sets and the Improved SVM Tian
More informationKeywords: Image complexity, PSNR, Levenberg-Marquardt, Multi-layer neural network.
Global Journal of Computer Science and Technology Volume 11 Issue 3 Version 1.0 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172
More informationBehavior Analysis in Crowded Environments. XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011
Behavior Analysis in Crowded Environments XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011 Behavior Analysis in Sparse Scenes Zelnik-Manor & Irani CVPR
More informationCHAPTER 8 FACTOR EXTRACTION BY MATRIX FACTORING TECHNIQUES. From Exploratory Factor Analysis Ledyard R Tucker and Robert C.
CHAPTER 8 FACTOR EXTRACTION BY MATRIX FACTORING TECHNIQUES From Exploratory Factor Analysis Ledyard R Tucker and Robert C MacCallum 1997 180 CHAPTER 8 FACTOR EXTRACTION BY MATRIX FACTORING TECHNIQUES In
More informationA progressive method to solve large-scale AC Optimal Power Flow with discrete variables and control of the feasibility
A progressive method to solve large-scale AC Optimal Power Flow with discrete variables and control of the feasibility Manuel Ruiz, Jean Maeght, Alexandre Marié, Patrick Panciatici and Arnaud Renaud manuel.ruiz@artelys.com
More informationCS1112 Spring 2014 Project 4. Objectives. 3 Pixelation for Identity Protection. due Thursday, 3/27, at 11pm
CS1112 Spring 2014 Project 4 due Thursday, 3/27, at 11pm You must work either on your own or with one partner. If you work with a partner you must first register as a group in CMS and then submit your
More information1604 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE 2014
1604 JOURNAL OF SOFTWARE, VOL. 9, NO. 6, JUNE 2014 Combining various trust factors for e-commerce platforms using Analytic Hierarchy Process Bo Li a, Yu Zhang b,, Haoxue Wang c, Haixia Xia d, Yanfei Liu
More informationAn introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY. Harrison H. Barrett University of Arizona Tucson, AZ
An introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY Harrison H. Barrett University of Arizona Tucson, AZ Outline! Approaches to image quality! Why not fidelity?! Basic premises of the task-based approach!
More informationAn Overview of Knowledge Discovery Database and Data mining Techniques
An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,
More informationSCHEDULING IN CLOUD COMPUTING
SCHEDULING IN CLOUD COMPUTING Lipsa Tripathy, Rasmi Ranjan Patra CSA,CPGS,OUAT,Bhubaneswar,Odisha Abstract Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
More informationResearch on the Performance Optimization of Hadoop in Big Data Environment
Vol.8, No.5 (015), pp.93-304 http://dx.doi.org/10.1457/idta.015.8.5.6 Research on the Performance Optimization of Hadoop in Big Data Environment Jia Min-Zheng Department of Information Engineering, Beiing
More informationAn Environment Model for N onstationary Reinforcement Learning
An Environment Model for N onstationary Reinforcement Learning Samuel P. M. Choi Dit-Yan Yeung Nevin L. Zhang pmchoi~cs.ust.hk dyyeung~cs.ust.hk lzhang~cs.ust.hk Department of Computer Science, Hong Kong
More informationResearch on Clustering Analysis of Big Data Yuan Yuanming 1, 2, a, Wu Chanle 1, 2
Advanced Engineering Forum Vols. 6-7 (2012) pp 82-87 Online: 2012-09-26 (2012) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/aef.6-7.82 Research on Clustering Analysis of Big Data
More informationPrice Prediction of Share Market using Artificial Neural Network (ANN)
Prediction of Share Market using Artificial Neural Network (ANN) Zabir Haider Khan Department of CSE, SUST, Sylhet, Bangladesh Tasnim Sharmin Alin Department of CSE, SUST, Sylhet, Bangladesh Md. Akter
More information