Parallel Data Selection Based on Neurodynamic Optimization in the Era of Big Data


 Arthur Burns
 2 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 Winnerstakeall Operation The kwinnerstakeall (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 Winnerstakeall 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 Winnerstakeall 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 QPbased PrimalDual Network
13 QPbased Projection Network
14 LPbased Projection Network
15 QPbased Simplified Dual Net
16 LPbased Discontinuous Network
17 Discontinuous Activation Function
18 Convergence Conditions
19 Simulation Results
20 Simulation Results (cont d)
21 Simulation Results (cont d)
22 QPbased Discontinuous Network
23 Discontinuous Activation Function
24 Convergence Condition
25 Simulation Results
26 Simulation Results(cont d)
27 Simulation Results (cont d)
28 QPbased Improved Dual Network
29 Model Comparisons Model Number of layer(s) Number of neuron(s) Number of connections LPbased primaldual network QPbased primaldual network 4 3n + 1 6n n + 1 6n + 2 LPbased projection network 2 n + 1 2n + 2 QPbased projection network QPbased simplified dual network 2 n + 1 2n n 3n LPbased discontinuous net 1 n 2n QPbased discontinuous network QPbased improved dual network 1 n 2n 1 1 n
30 Simulation Results
31 Discretetime 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 networkbased 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 Discretetime 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 n1 kwta networks without the need for computing the last item. As a result, only n1 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 Rankorder 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 realtime signal processing applications.
69 Rankorder 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 worldwide 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 topk 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 Filmdirectoractorwriter 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 kwinnerstakeall operations. kwinnerstakeall neural networks provide parallel distributed computational models with guaranteed global convergence to the optimal solutions. Neurodynamic optimization approaches are more suitable for realtime applications with big data. GPUbased 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 Yatsen 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
The Analysis of Analog Circuits Fault Diagnosis Methods
6th International Conference on Electronic, Mechanical, Information and Management (EMIM 2016) The Analysis of Analog Circuits Fault Diagnosis Methods Zhiqiang Zhang1, a * and Aihua Zhang1, b 1 College
More informationI Nrecentyears,therehavebeenalotofresearchworksfocusing
656 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 52, NO. 10, OCTOBER 2005 Approximation of Dynamical TimeVariant Systems by ContinuousTime Recurrent Neural Networks XiaoDong Li,
More informationThe Improved Neural Network Algorithm of License Plate Recognition
, pp. 4954 http://dx.doi.org/10.14257/ijsip.2015.8.5.06 The Improved Neural Network Algorithm of License Plate Recognition Jingwei Dong 1, Meiting Sun 1, Gengrui Liang 2 and Kui Jin 1 1 School of MeasureControl
More informationLinear Algebra Methods for Data Mining
Linear Algebra Methods for Data Mining Saara Hyvönen, Saara.Hyvonen@cs.helsinki.fi Spring 2007 Mining the web: PageRank and Power Iteration Linear Algebra Methods for Data Mining, Spring 2007, University
More informationRanking Web Pages. Tim Chartier and Anne Greenbaum. Winter Department of
Department of Mathematics @ Winter 2008 Google Have a question? Looking for an old friend? Need a reference for a paper? A popular and often effective form of information acquisition is submitting queries
More informationTHE class of dynamic systems consisting of linear subsystems
390 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 51, NO. 3, MARCH 2006 Recursive Identification for Wiener Model With Discontinuous PieceWise Linear Function HanFu Chen, Fellow, IEEE Abstract This paper
More informationAn Artificial Neural Network for Data Mining
An Artificial Neural Network for Data Mining Dimitrios C. Kyritsis Dept. of Mechanical Engineering, Khalifa Univ. of Science, Technology and Research, Abu Dhabi, United Arab Emirates Abstract: Data mining
More informationEE 5322 Neural Networks Notes
EE 5322 Neural Networks Notes his short note on neural networks is based on [1], [2]. Much of this note is based almost entirely on examples and figures taken from these two sources. he MALAB Neural Networks
More informationAnalecta Vol. 8, No. 2 ISSN 20647964
EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,
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 informationAutomated 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 informationSelf Organizing Maps: Properties and Applications
Self Organizing Maps: Properties and Applications Neural Computation : Lecture 17 John A. Bullinaria, 2015 1. The SOM Architecture and Algorithm 2. Properties of the Feature Map Approximation of the Input
More informationHamming Codes. Chapter Basics
Chapter 4 Hamming Codes In the late 1940 s Claude Shannon was developing information theory and coding as a mathematical model for communication. At the same time, Richard Hamming, a colleague of Shannon
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 informationLargescale Data Mining: MapReduce and Beyond Part 2: Algorithms. Spiros Papadimitriou, IBM Research Jimeng Sun, IBM Research Rong Yan, Facebook
Largescale 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 informationAn Advanced Technique for Removal Of Salt & Pepper Noise In Images
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:23197242 Volume 2 Issue 9 September 2013 Page No. 28562860 An Advanced Technique for Removal Of Salt & Pepper Noise In Images
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 informationL0TV: A New Method for Image Restoration in the Presence of Impulse Noise
IEEE Conference on Computer Vision and Pattern Recognition 2015 L0TV: A New Method for Image Restoration in the Presence of Impulse Noise Ganzhao Yuan 1, Bernard Ghanem 2 1. South China University of Technology
More informationMODULE 15 Clustering Large Datasets LESSON 34
MODULE 15 Clustering Large Datasets LESSON 34 Incremental Clustering Keywords: Single Database Scan, Leader, BIRCH, Tree 1 Clustering Large Datasets Pattern matrix It is convenient to view the input data
More informationKANGSHUN LI, YUANXIANG LI, HAIFANG MO, ZHANGXIN CHEN
A NEW ALGORITHM OF EVOLVING ARTIFICIAL NEURAL NETWORKS VIA GENE EXPRESSION PROGRAMMING KANGSHUN LI, YUANXIANG LI, HAIFANG MO, ZHANGXIN CHEN Abstract. In this paper a new algorithm of learning and evolving
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 informationWE consider an error correction scheme for arraylike
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 45, NO. 7, NOVEMBER 1999 2339 A Decoding Algorithm Restrictions for Array Codes Christoph Haslach, Student Member, IEEE, and A. J. Han Vinck, Senior Member,
More informationDistributed computing: index building and use
Distributed computing: index building and use Distributed computing Goals Distributing computation across several machines to Do one computation faster  latency Do more computations in given time  throughput
More informationRegression Using Support Vector Machines: Basic Foundations
Regression Using Support Vector Machines: Basic Foundations Technical Report December 2004 Aly Farag and Refaat M Mohamed Computer Vision and Image Processing Laboratory Electrical and Computer Engineering
More informationarxiv: v1 [cs.cv] 4 Feb 2016
RANDOM FEATURE MAPS VIA A LAYERED RANDOM PROJECTION (LARP) FRAMEWORK FOR OBJECT CLASSIFICATION A. G. Chung, M. J. Shafiee, and A. Wong Vision & Image Processing Research Group, System Design Engineering
More informationForming and implementing a hyperchaotic system with rich dynamics
Forming and implementing a hyperchaotic system with rich dynamics Liu WenBo( ) a), Wallace K. S. Tang( ) b), and Chen GuanRong( ) b) a) College of Automatic Engineering, Nanjing University of Aeronautics
More information1 Basics Give a short 12 sentence answer to the following questions.
Due 2pm, January 12 th, via Moodle You are free to collaborate on all of the problems, subject to the collaboration policy stated in the syllabus The first two sections (Basics and Cross Validation) are
More informationSynchronization of spatiotemporal chaos in a class of complex dynamical networks
Synchronization of spatiotemporal chaos in a class of complex dynamical networks Zhang QingLing( ) a) and Lü Ling( ) a)b) a) Institute of System Science, Northeastern University, Shenyang 110004, China
More informationContentBased Image Retrieval Challenges & Opportunities
ContentBased Image Retrieval Challenges & Opportunities Jianping Fan Department of Computer Science University of North Carolina at Charlotte Charlotte, NC 28223 Networks How can I access image/video
More informationFast Analysis Method for both Coupled and Uncoupled BinaryOutput Cloning Templates
Fast Analysis Method for both Coupled and Uncoupled BinaryOutput Cloning Templates Yohann Bénédic David Monnin Jean Mercklé Abstract The output of a cellular neural network can always be described by
More informationLoad balancing in a heterogeneous computer system by selforganizing Kohonen network
Bull. Nov. Comp. Center, Comp. Science, 25 (2006), 69 74 c 2006 NCC Publisher Load balancing in a heterogeneous computer system by selforganizing Kohonen network Mikhail S. Tarkov, Yakov S. Bezrukov Abstract.
More informationDimensionality Reduction with PCA
Dimensionality Reduction with PCA Ke Tran May 24, 2011 Introduction Dimensionality Reduction PCA  Principal Components Analysis PCA Experiment The Dataset Discussion Conclusion Why dimensionality reduction?
More informationAN ITERATIVE EIGENDECOMPOSITION APPROACH TO BLIND SOURCE SEPARATION. Ana Maria Tomé
AN ITERATIVE EIGENDECOMPOSITION APPROACH TO BLIND SOURCE SEPARATION Ana Maria Tomé Dep. Electrónica e Telecomunicações/IEETA Universidade AveiroAveiroPortugal email:ana@ieeta.pt ABSTRACT In this work
More informationBatchLearning SelfOrganizing Map with FalseNeighbor Degree for Effective SelfOrganization
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS Abstract TECHNICAL REPORT OF IEICE. BatchLearning SelfOrganizing Map with FalseNeighbor Degree for Effective SelfOrganization Haruna
More informationComputer Vision Group Prof. Daniel Cremers. 14. Sampling Methods
Prof. Daniel Cremers 14. Sampling Methods Sampling Methods Sampling Methods are widely used in Computer Science as an approximation of a deterministic algorithm to represent uncertainty without a parametric
More informationLearning vector Quantization
POLYTECHNIC UNIVERSITY Department of Computer and Information Science Learning vector Quantization K. Ming Leung Abstract: Learning vector Quantization is a NN invented by Kohonen for pattern classification.
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, 766771 Open Access Research on Application of Neural Network in Computer Network
More informationThe parallel design and implementation of the PCNN algorithm based on the visual perception information
Applied Mechanics and Materials Online: 20130213 ISSN: 16627482, Vols. 291294, pp 29362940 doi:10.4028/www.scientific.net/amm.291294.2936 2013 Trans Tech Publications, Switzerland The parallel design
More informationGraph Based Image Saliency Detection
International Conference on Civil, Materials and Environmental Sciences (CMES 015) Graph Based Image Saliency Detection Ye Huang Institute of Optics and Electronics Chinese Academy of Sciences, Sichuan
More informationCS345 Data Mining. Page Rank Variants
CS345 Data Mining Page Rank Variants Review Page Rank Web graph encoded by matrix M N N matrix (N = number of web pages) M ij = 1/ O(j) iff there is a link from j to i M ij = 0 otherwise O(j) = set of
More informationEFFICIENT DATA PREPROCESSING FOR DATA MINING USING NEURAL NETWORKS
EFFICIENT DATA PREPROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Dr. Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil. Assistant Professor, AdhiparasakthiCollege
More informationFast Compressed Sensing Based High Resolution Image Reconstruction
International Journal of New Technology and Research (IJNTR) ISSN:244116, Volume2, Issue3, March 16 Pages 7176 Fast Compressed Sensing Based High Resolution Image Reconstruction Dr G Vijay Kumar Abstract
More informationComputing for Scientists  Lab 3
Computing for Scientists  Lab 3 CS 1340 Dr. Mihail Department of Computer Science Valdosta State University February 2, 2015 1 Introduction In this lab, you will learn about random numbers. Many engineering
More informationThe Application of Neural Network in the Technology of Image Processing
The Application of Neural Network in the Technology of Image Processing Weibin Hong, Wei Chen, and Rui Zhang Abstract Nowadays, we make use of the digital quantity to store and recover the information
More informationFault Analysis in Software with the Data Interaction of Classes
, pp.189196 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 informationJob Scheduling Problem with Fuzzy Neural Network by using the MapReduce Model in a Cloud Environment
Job Scheduling Problem with Fuzzy Neural Network by using the MapReduce Model in a Cloud Environment Forough zare Department of Computer, Science and Research Branch, Islamic Azad University Khouzestan,
More informationAutonomous Vehicle Steering Characteristics of ANNs
EE459 Neural Networks Backpropagation Kasin Prakobwaitayakit Department of Electrical Engineering Chiangmai University Background Artificial neural networks (ANNs) provide a general, practical method for
More informationLargeScale Data Sets Clustering Based on MapReduce and Hadoop
Journal of Computational Information Systems 7: 16 (2011) 59565963 Available at http://www.jofcis.com LargeScale Data Sets Clustering Based on MapReduce and Hadoop Ping ZHOU, Jingsheng LEI, Wenjun YE
More informationSparsity Based Regularization
Sparsity Based Regularization Lorenzo Rosasco 9.520 Class 11 March 11, 2009 About this class Goal To introduce sparsity based regularization with emphasis on the problem of variable selection. To discuss
More informationChapter 2 Notation and Norms
Chapter 2 Notation and Norms 2.1 Introduction This chapter recalls the usual convention for distinguishing scalars, vectors, and matrices. Vetter s notation for matrix derivatives is then explained, as
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 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 informationLearning Targets for Mathematics Milwaukee Public Schools
Learning Targets for Mathematics Milwaukee Public Schools Grades K 4 Mathematics Learning Targets Milwaukee Public Schools 2003 2004 Number Operations & Relationships Geometry Kindergarten (1) Use strategies
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 informationPreserving Class Discriminatory Information by. Contextsensitive Intraclass Clustering Algorithm
Preserving Class Discriminatory Information by Contextsensitive Intraclass Clustering Algorithm Yingwei Yu, Ricardo GutierrezOsuna, and Yoonsuck Choe Department of Computer Science Texas A&M University
More informationSampling Methods: Particle Filtering
Penn State Sampling Methods: Particle Filtering CSE586 Computer Vision II CSE Dept, Penn State Univ Penn State Recall: Importance Sampling Procedure to estimate E P (f(x)): 1) Generate N samples x i from
More informationBINARY SOLUTIONS FOR OVERDETERMINED SYSTEMS OF LINEAR EQUATIONS
Page 1 of 13 BINARY SOLUTIONS FOR OVERDETERMINED SYSTEMS OF LINEAR EQUATIONS SUBHENDU DAS, CCSI, CALIFORNIA, USA Abstract: This paper presents a finite step method for computing the binary solution to
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 informationFast MonteCarlo Low Rank Approximations for Matrices. Shmuel Friedland University of Illinois at Chicago
Fast MonteCarlo Low Rank Approximations for Matrices Shmuel Friedland University of Illinois at Chicago joint work with M. Kaveh, A. Niknejad and H. Zare IEEE SoSE 2006, LA, April 25, 2006 http://www.math.uic.edu/
More informationClusters With CoreTail Hierarchical Structure And Their Applications To Machine Learning Classification
Clusters With CoreTail Hierarchical Structure And Their Applications To Machine Learning Classification Dmitriy Fradkin Ilya B. Muchnik Dept. of Computer Science DIMACS dfradkin@paul.rutgers.edu muchnik@dimacs.rutgers.edu
More informationLDPC Decoder using Low Complexity Min Sum Algorithm with QPSK Modulation 1 V.Hephzibah, 2 S.J.Jona Priyaa, 3 R.Jesintha, 4 D.M.
Vol., Issue. 3, April 25 ISSN (Online): 2349828 LDPC Decoder using Low Complexity Min Sum Algorithm with QPSK Modulation V.Hephzibah, 2 S.J.Jona Priyaa, 3 R.Jesintha, 4 D.M.Joice Anbiya, 5 Mrs. Jeevitha,2,3,4
More informationEFFICIENT DATA PREPROCESSING FOR DATA MINING
EFFICIENT DATA PREPROCESSING 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 informationTARJAN S ALGORITHM IN COMPUTING PAGERANK
TARJAN S ALGORITHM IN COMPUTING PAGERANK Ivana Pultarová Department of Mathematics, Faculty of Civil Engineering, Czech Technical University in Prague Abstract As a core problem in computing PageRank a
More informationSimple Neural Networks for Pattern Classification
POLYTECHNIC UNIVERSITY Department of Computer and Information Science Simple Neural Networks for Pattern Classification K. Ming Leung Abstract: A simple neural network capable of classifying patterns into
More informationEfficient Removal of Impulse Noise from Digital Images
Efficient Removal of Impulse Noise from Digital Images IEEE Transactions on Consumer Electronics, vol. 5, no., May 006 W. Luo School of Electrical Engineering and Computer Science Kyungpook National Univ.
More informationCS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 RealTime 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 informationLayout Based Visualization Techniques for Multi Dimensional Data
Layout Based Visualization Techniques for Multi Dimensional Data Wim de Leeuw Robert van Liere Center for Mathematics and Computer Science, CWI Amsterdam, the Netherlands wimc,robertl @cwi.nl October 27,
More informationDesigning Fast Distributed Iterations via Semidefinite Programming
Designing Fast Distributed Iterations via Semidefinite Programming Lin Xiao and Stephen Boyd Stanford University (Joint work with Persi Diaconis and Jun Sun) 5/14/2004 Workshop on Large Scale Nonlinear
More informationJames B. Fenwick, Jr., Program Director and Associate Professor Ph.D., The University of Delaware FenwickJB@appstate.edu
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 informationSimulationbased design improvement of a superconductive magnet by mixedinteger nonlinear surrogate optimization
Simulationbased design improvement of a superconductive magnet by mixedinteger nonlinear surrogate optimization T. Hemker, O. von Stryk, H. De Gersem, and T. Weiland The numerical optimization of continuous
More informationFace Recognition using Principle Component Analysis
Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA Summary This is the summary of the basic idea about PCA
More informationMeanShift Tracking with Random Sampling
1 MeanShift 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 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 informationCISC 4631 Data Mining Lecture 11:
CISC 4631 Data Mining Lecture 11: Neural Networks Biological Motivation Can we simulate the human learning process? Two schools modeling biological learning process obtain highly effective algorithms,
More informationA Genetic Algorithm Approach to Scheduling Communications for a Class of Parallel SpaceTime Adaptive Processing Algorithms
A Genetic Algorithm Approach to Scheduling Communications for a Class of Parallel SpaceTime Adaptive Processing Algorithms Jack M. West and John K. Antonio School of Computer Science University of Oklahoma
More informationDISCRETE COSINE TRANSFORMS
DISCRETE COSINE TRANSFORMS ~ Jennie G. Abraham Fall 2009, EE5355 Reference Book: THE TRANSFORM AND DATA COMPRESSION HANDBOOK, edited by K.R. Rao and P.C. Yip 4.0 Transform Introduction In general, there
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 informationA Visual Measurement of Fish Locomotion Based on Deformable Models
A Visual Measurement of Fish Locomotion Based on Deformable Models Chunlei Xia 1,2, Yan Li 3, and JangMyung Lee 2 1 Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003,
More informationTrimmed Median Filters for Salt and Pepper Noise Removal
Trimmed Median Filters for Salt and Pepper Noise Removal S. Athi Narayanan, G. Arumugam, Prof. Kamal Bijlani Amrita ELearning Research Lab, Amrita University, Kollam, Kerala, India Abstract: With this
More informationResearch on Semantic Web Service Composition Based on Binary Tree
, pp.133142 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 informationTabla Strokes Recognition. Mihir Sarkar
Tabla Strokes Recognition Mihir Sarkar Tabla? The tabla is a pair of hand drums from North India. They are played with the fingers and palms of both hands. The right drum (from a player s perspective)
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 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 informationSPARSE REPRESENTATIONS APPLICATIONS ON COMPUTER VISION AND PATTERN RECOGNITION
SPARSE REPRESENTATIONS APPLICATIONS ON COMPUTER VISION AND PATTERN RECOGNITION Computer Vision Group Electronics Laboratory Physics Department University of Patras www.upcv.upatras.gr www.ellab.physics.upatras.gr
More informationFactoring Symmetric Indefinite Matrices
Factoring Symmetric Indefinite Matrices Voicu Chis April 17, 2007 Describing the problem and motivating Motivation. IPM requires solving: 0 m A 0 m,n λ A T 0 n I n x = b 0 n,m S X s A M m,n (R) full rank,
More informationPerformance Evaluation and Prediction of ITOutsourcing Service Supply Chain based on Improved SCOR Model
Performance Evaluation and Prediction of ITOutsourcing Service Supply Chain based on Improved SCOR Model 1, 2 1 International School of Software, Wuhan University, Wuhan, China *2 School of Information
More informationImage Denoising Algorithm Based on Non Related Dictionary Learning
, pp.95100 http://dx.doi.org/10.1457/astl.016.13.19 Image Denoising Algorithm Based on Non Related Dictionary Learning Yao Nan 1, Wang KaiSheng and Cai Yue 3 1 Department of Jiangsu Electric Power Company
More informationAn Ontology Based Text Mining
An Ontology Based Text Mining Kuwar Aditya, Bhalekar Arjun, Bade Ankush Department of Computer, DYPIT, University of pune, India AbstractResearch project selection is important task for government and
More informationSparse MatrixMatrix Multiplication for Accelerating Parallel Graph Computations
Sparse MatrixMatrix Multiplication for Accelerating Parallel Graph Computations Aydin Buluc John R. Gilbert University of California, Santa Barbara SIAM CSE 2009 March 2, 2009 1 Support: DOE Office of
More informationAnalysis of the Largest Normalized Residual Test Robustness for Measurements Gross Errors Processing in the WLS State Estimator
Analysis of the Largest Normalized Residual Test Robustness for s Gross Errors Processing in the WLS State Estimator Breno CARVALHO Electrical Engineering Department, University of São Paulo São Carlos,
More informationClustering With EM and KMeans
Clustering With EM and KMeans Neil Alldrin Department of Computer Science University of California, San Diego La Jolla, CA 97 nalldrin@cs.ucsd.edu Andrew Smith Department of Computer Science University
More informationAN EFFECTIVE DENOISING METHOD FOR MEDICAL ULTRASOUND IMAGE
30 th November 0. Vol. 45 No. 0050 JATIT & LLS. All rights reserved. ISSN: 998645 www.jatit.org EISSN: 87395 AN EFFECTIVE DENOISING METHOD FOR MEDICAL ULTRASOUND IMAGE HUADONG WANG School of Computer
More informationA Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster
, pp.1120 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 informationA Literature Review on Methodology & Fundamentals of Development of Mathematical Model through Simulation of Artificial Neural Network
A Literature Review on Methodology & Fundamentals of Development of Mathematical Model through Simulation of Artificial Neural Network Mr. P. A. Chandak #1, Ms. A. R. Lende #2, Mr. J. P. Modak 3 #3 ABSTRACT
More informationDifferential Evolution Particle Swarm Optimization for Digital Filter Design
Differential Evolution Particle Swarm Optimization for Digital Filter Design Bipul Luitel, and Ganesh K. Venayagamoorthy, Abstract In this paper, swarm and evolutionary algorithms have been applied for
More informationAdvanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras
Advanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture No.  15 Branch and Bound Algorithm for Integer Programming Today we continue
More informationMULTIPLE CHOICE QUESTIONS DECISION SCIENCE
MULTIPLE CHOICE QUESTIONS DECISION SCIENCE 1. Decision Science approach is a. Multidisciplinary b. Scientific c. Intuitive 2. For analyzing a problem, decisionmakers should study a. Its qualitative aspects
More informationClassification of Fast Magnetic Resonance Image Reconstruction Using Matching Pursuit Family Algorithm
Classification of Fast Magnetic Resonance Image Reconstruction Using Matching Pursuit Family Algorithm Aldi Gunawan December 10, 2010 aldi@stanford.edu 1 Introduction Magnetic Resonance Imaging (MRI) is
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 informationPhase space reconstruction of chaotic dynamical system based on wavelet decomposition
Chin Phys B Vol 20, No 2 (2011) 020505 Phase space reconstruction of chaotic dynamical system based on wavelet decomposition You RongYi( ) and Huang XiaoJing( ) Department of Physics, School of Science,
More information