NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS


 Verity Daniel
 2 years ago
 Views:
Transcription
1 NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS N. K. Bose HRBSystems Professor of Electrical Engineering The Pennsylvania State University, University Park P. Liang Associate Professor of Electrical Engineering University of California, Riverside McGrawHill, Inc. New York St. Louis San Francisco Auckland Bogota Caracas Lisbon London Madrid Mexico City Milan Montreal New Delhi San Juan Singapore Sydney Tokyo Toronto
2 CONTENTS List of Figures List of Tables Preface List of Acronyms Glossary of Notations xiv xix xxi xxix xxxii I Fundamentals 1 Basics of Neuroscience and Artificial Neuron Models The Brain as a Neural Network Basic Properties of Neurons 91:2.1 Structure of a Neuron Dendritic Tree Action Potential and Its Propagation Synapses Connection Patterns between Neurons An Example: A Motion Detection Neuron Neuron Models McCullochPitts Model Neuron Models with Continuous Transfer Characteristics Other Neuron Models Conclusions and Suggestions 28 Problems 31 2 Graphs Terminology and Preliminaries Special Types of Graphs Directed Graphs 41 ix
3 X CONTENTS 2.4 Matrix Representation of Graphs Adjacency Matrix Interconnection Matrix Topological Invariants Euler and Schlaefli Invariants Genus Thickness Some Other Topological Invariants Voronoi Diagrams and Deiaunay Tessellation Conclusions and Suggestions 55 Problems 57 3 Algorithms Computational Complexity: P and NPComplete Problems ShortestPath and MaxFlow MinCut Problems Dijkstra's ShortestPath Algorithm MaxFlow MinCut Algorithm Interconnection and Routing Algorithms Problem Formulation Minimal Spanning Tree (MST) Algorithms Minimal Fermat Tree (MFT) Problem Traveling Salesperson (TS) Problem Steiner Minimal Tree (SMT) Placement and Partitioning Placement Partitioning Parallel Computation Associative Memory The Linear Associator: Solution by Hebbian Rule The Linear Associator: Solution by Generalized Inverse Implementation of Associative Memory Conclusions 106 Problems 108 II Feedforward Networks 4 Perceptrons and the LMS Algorithm Rosenblatt's Perceptron Definitions Linear Separability of Training Patterns Perceptron Learning Algorithms Derivation of the Perceptron Algorithm as Gradient Descent The Perceptron Convergence Theorem The WidrowHoff LMS Algorithm Order of a Predicate and a Perceptron Conclusions and Suggestions 147 Problems 148
4 CONTENTS XI 5 Multilayer Networks Exact and Approximate Representation Using Feedforward Networks Exact Representation: Kolmogorov's Theorem and Its Consequences Approximate Representations Fixed Multilayer Feedforward Network Training by Backpropagation Implementation Considerations for Backpropagation Variants of BPA Temporal Signal Recognition and Prediction Structural Training of Multilayer Feedforward Networks Algorithm for Design Based on VoD Robustness and Size Issues Unsupervised and Reinforcement Learning Principal Component Analysis Networks SelfOrganization in a Perceptual Network Reinforcement Learning The Probabilistic Neural Network Conclusions and Suggestions 209 Problems Complexity of Learning Using Feedforward Networks Learnability in ANN The Problem of Loading Using an Appropriate Network to Get Around Intractability Generalizability of Learning VC Dimension and Generalization 232 \ "6.2.2 Sufficient Conditions for Valid Generalization ' in Feedforward Networks Necessary Conditions for Valid Generalization in Feedforward Networks Discussions and Ways to Improve Generalization Space Complexity of Feedforward Networks Order of a Function and the Complexity of a Network High Connectivity in Analog Neural Computations Summary and Discussion 250 Problems AdaptiveStructure Networks Growth Algorithms The Upstart Algorithm Learning by Divide and Conquer Other Growth Algorithms 265
5 XH CONTENTS 7.2 Networks with Nonlinear Synapses and Nonlinear Synaptic Contacts QuasiPolynomial Synapses and Product Synaptic Contacts Generalization of Learning and Hardware Considerations Conclusions and Suggestions 278 Problems 281 III Recurrent Networks 8 Symmetric and Asymmetric Recurrent Networks Symmetric Hopfield Networks and Associative Memory Convergence Proofs Computation in a Network and Minimum Cuts in a Graph Capacity and Spurious Memory Correlated Patterns Hopfield Networks with Variations in the Connection Weights Bidirectional Associative Memory Symmetric Networks with Analog Units Analog Hopfield Networks Convergence Proof Relation between Stable States of Discrete and Analog Hopfield Networks Cellular Neural Networks Seeking the Global Minimum: Simulated Annealing Simulated Annealing in Optimization Stochastic Networks: Applying Simulated Annealing to Hopfield Networks A Learning Algorithm for the Boltzmann Machine Learning the Underlying Structure of an Environment The Learning Procedure Mean Field Theory and the Deterministic Boltzmann Machine Asymmetric Recurrent Networks Phase Transition from Stationary to Chaotic Spatial and Temporal Patterns Learning in Asymmetric Networks: Recurrent Backpropagation Summary and Discussion 340 Problems Competitive Learning and SelfOrganizing Networks Unsupervised Competitive Learning Two Phases of Competitive Learning 346
6 CONTENTS XUl Using a Competitive Learning Network for Associative Memory Adaptive Resonant Networks The ART1 Clustering Algorithm The ART1 Network SelfOrganizing Feature Maps The Kohonen Map Analysis of Kohonen Maps Adaptive and Learning Vector Quantization TwoDimensional Topographic Maps A Multilayer SelfOrganizing Feature Map Hybrid Learning Counterpropagation Network Regularizing Networks and Radial Basis Functions Summary and Discussion Problems IV Applications of Neural Networks 10 Neural Network Approaches to Solving Hard Problems The Traveling Salesperson Problem Multitarget Tracking Time Series Prediction Talking Network and Phonetic Typewriter Speech Generation Speech Recognition Autonomous Vehicle Navigation Handwritten Digit Recognition Image Compression by a Multilayer Feedforward {   Structure Trained through Backpropagation 430 I 10.8 Character Retrieval Using the Discrete Hopfield Network Visual Processing Networks Conclusion and Discussion 443 References 447 Appendix A Basis of GradientBased Optimization Methods 463 A.I The Gradient Descent Method 464 A.2 Newton's Method 467 A.3 The Conjugate Gradient Method 468 A.4 Constrained Optimization 469 Bibliography 470 Index 471
A TUTORIAL. BY: Negin Yousefpour PhD Student Civil Engineering Department TEXAS A&M UNIVERSITY
ARTIFICIAL NEURAL NETWORKS: A TUTORIAL BY: Negin Yousefpour PhD Student Civil Engineering Department TEXAS A&M UNIVERSITY Contents Introduction Origin Of Neural Network Biological Neural Networks ANN Overview
More informationNEURAL NETWORKS A Comprehensive Foundation
NEURAL NETWORKS A Comprehensive Foundation Second Edition Simon Haykin McMaster University Hamilton, Ontario, Canada Prentice Hall Prentice Hall Upper Saddle River; New Jersey 07458 Preface xii Acknowledgments
More informationLecture 1: Introduction to Neural Networks Kevin Swingler / Bruce Graham
Lecture 1: Introduction to Neural Networks Kevin Swingler / Bruce Graham kms@cs.stir.ac.uk 1 What are Neural Networks? Neural Networks are networks of neurons, for example, as found in real (i.e. biological)
More informationNeural networks. Chapter 20, Section 5 1
Neural networks Chapter 20, Section 5 Chapter 20, Section 5 Outline Brains Neural networks Perceptrons Multilayer perceptrons Applications of neural networks Chapter 20, Section 5 2 Brains 0 neurons of
More informationIntroduction to Neural Computation. Neural Computation
Introduction to Neural Computation Level 4/M Neural Computation Level 3 Website: http://www.cs.bham.ac.uk/~jxb/inc.html Lecturer: Dr. John A. Bullinaria John A. Bullinaria, 2015 Module Administration and
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  Multilayer networks What is a Support
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 informationIntroduction to Neural Networks
Introduction to Neural Networks 2nd Year UG, MSc in Computer Science http://www.cs.bham.ac.uk/~jxb/inn.html Lecturer: Dr. John A. Bullinaria http://www.cs.bham.ac.uk/~jxb John A. Bullinaria, 2004 Module
More informationOne Solution to XOR problem using Multilayer Perceptron having Minimum Configuration
International Journal of Science and Engineering Volume 3, Number 22015 PP: 3241 IJSE Available at www.ijse.org ISSN: 23472200 One Solution to XOR problem using Multilayer Perceptron having Minimum
More informationINTRODUCTION TO NEURAL NETWORKS
INTRODUCTION TO NEURAL NETWORKS Pictures are taken from http://www.cs.cmu.edu/~tom/mlbookchapterslides.html http://research.microsoft.com/~cmbishop/prml/index.htm By Nobel Khandaker Neural Networks An
More informationArtificial neural networks
Artificial neural networks Now Neurons Neuron models Perceptron learning Multilayer perceptrons Backpropagation 2 It all starts with a neuron 3 Some facts about human brain ~ 86 billion neurons ~ 10 15
More informationFeedForward mapping networks KAIST 바이오및뇌공학과 정재승
FeedForward mapping networks KAIST 바이오및뇌공학과 정재승 How much energy do we need for brain functions? Information processing: Tradeoff between energy consumption and wiring cost Tradeoff between energy consumption
More information6.2.8 Neural networks for data mining
6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural
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 informationNeural Networks. Neural network is a network or circuit of neurons. Neurons can be. Biological neurons Artificial neurons
Neural Networks Neural network is a network or circuit of neurons Neurons can be Biological neurons Artificial neurons Biological neurons Building block of the brain Human brain contains over 10 billion
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 informationNumerical Methods for Engineers
Steven C. Chapra Berger Chair in Computing and Engineering Tufts University RaymondP. Canale Professor Emeritus of Civil Engineering University of Michigan Numerical Methods for Engineers With Software
More informationIntroduction to Neural Networks : Revision Lectures
Introduction to Neural Networks : Revision Lectures John A. Bullinaria, 2004 1. Module Aims and Learning Outcomes 2. Biological and Artificial Neural Networks 3. Training Methods for Multi Layer Perceptrons
More informationMethod of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks
Method of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks Ph. D. Student, Eng. Eusebiu Marcu Abstract This paper introduces a new method of combining the
More informationModular Neural Networks
16 Modular Neural Networks In the previous chapters we have discussed different models of neural networks linear, recurrent, supervised, unsupervised, selforganizing, etc. Each kind of network relies
More informationIntroduction to Artificial Neural Networks
POLYTECHNIC UNIVERSITY Department of Computer and Information Science Introduction to Artificial Neural Networks K. Ming Leung Abstract: A computing paradigm known as artificial neural network is introduced.
More informationNeural Networks algorithms and applications
Neural Networks algorithms and applications By Fiona Nielsen 4i 12/122001 Supervisor: Geert Rasmussen Niels Brock Business College 1 Introduction Neural Networks is a field of Artificial Intelligence
More informationSoftware Project Management (Second Edition)
Software Project Management (Second Edition) Bob Hughes and Mike Cotterell, School of Information Management, University of Brighton The McGrawHill Companies London Burr Ridge, IL New York St Louis San
More informationData Mining Using Neural Networks: A Guide for Statisticians
Data Mining Using Neural Networks: A Guide for Statisticians Basilio de Bragança Pereira UFRJ  Universidade Federal do Rio de Janeiro Calyampudi Radhakrishna Rao PSU  Penn State University June 2009
More informationSEMINAR OUTLINE. Introduction to Data Mining Using Artificial Neural Networks. Definitions of Neural Networks. Definitions of Neural Networks
SEMINAR OUTLINE Introduction to Data Mining Using Artificial Neural Networks ISM 611 Dr. Hamid Nemati Introduction to and Characteristics of Neural Networks Comparison of Neural Networks to traditional
More informationIntroduction to Artificial Neural Networks. Introduction to Artificial Neural Networks
Introduction to Artificial Neural Networks v.3 August Michel Verleysen Introduction  Introduction to Artificial Neural Networks p Why ANNs? p Biological inspiration p Some examples of problems p Historical
More information129: Artificial Neural Networks. Ajith Abraham Oklahoma State University, Stillwater, OK, USA 1 INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
129: Artificial Neural Networks Ajith Abraham Oklahoma State University, Stillwater, OK, USA 1 Introduction to Artificial Neural Networks 901 2 Neural Network Architectures 902 3 Neural Network Learning
More informationBuilding VPNs. NamKee Tan. With IPSec and MPLS. McGrawHill CCIE #4307 S&
Building VPNs With IPSec and MPLS NamKee Tan CCIE #4307 S& .jr.".. i McGrawHill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto
More informationIntroduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011
Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning
More informationNeural Networks and Support Vector Machines
INF5390  Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF539013 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines
More informationSoftware and Hardware Solutions for Accurate Data and Profitable Operations. Miguel J. Donald J. Chmielewski Contributor. DuyQuang Nguyen Tanth
Smart Process Plants Software and Hardware Solutions for Accurate Data and Profitable Operations Miguel J. Bagajewicz, Ph.D. University of Oklahoma Donald J. Chmielewski Contributor DuyQuang Nguyen Tanth
More informationDEC Networks and Architectures
DEC Networks and Architectures Carl Malamud Intertext Publications McGrawHill Book Company New York St. Louis San Francisco Auckland Bogota Hamburg London Madrid Mexico Milan Montreal New Delhi Panama
More informationNeural Network and Its Application in IR
UIUCLIS1999/5+IRG Neural Network and Its Application in IR Qin He Graduate School of Library and Information Science University of Illinois at UrbanaChampaign Spring, 1999 Abstract This is a literature
More informationLecture 6. Artificial Neural Networks
Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm
More informationA Time Series ANN Approach for Weather Forecasting
A Time Series ANN Approach for Weather Forecasting Neeraj Kumar 1, Govind Kumar Jha 2 1 Associate Professor and Head Deptt. Of Computer Science,Nalanda College Of Engineering Chandi(Bihar) 2 Assistant
More informationChapter 4: Artificial Neural Networks
Chapter 4: Artificial Neural Networks CS 536: Machine Learning Littman (Wu, TA) Administration icml03: instructional Conference on Machine Learning http://www.cs.rutgers.edu/~mlittman/courses/ml03/icml03/
More informationIntroduction to Machine Learning Using Python. Vikram Kamath
Introduction to Machine Learning Using Python Vikram Kamath Contents: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Introduction/Definition Where and Why ML is used Types of Learning Supervised Learning Linear Regression
More informationRatebased artificial neural networks and error backpropagation learning. Scott Murdison Machine learning journal club May 16, 2016
Ratebased artificial neural networks and error backpropagation learning Scott Murdison Machine learning journal club May 16, 2016 Murdison, Leclercq, Lefèvre and Blohm J Neurophys 2015 Neural networks???
More informationQUANTITATIVE METHODS. for Decision Makers. Mik Wisniewski. Fifth Edition. FT Prentice Hall
Fifth Edition QUANTITATIVE METHODS for Decision Makers Mik Wisniewski Senior Research Fellow, Department of Management Science, University of Strathclyde Business School FT Prentice Hall FINANCIAL TIMES
More informationNeural Networks. Introduction to Artificial Intelligence CSE 150 May 29, 2007
Neural Networks Introduction to Artificial Intelligence CSE 150 May 29, 2007 Administration Last programming assignment has been posted! Final Exam: Tuesday, June 12, 11:302:30 Last Lecture Naïve Bayes
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 informationPractical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING
Practical Applications of DATA MINING Sang C Suh Texas A&M University Commerce r 3 JONES & BARTLETT LEARNING Contents Preface xi Foreword by Murat M.Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction
More informationIntroduction to Neural Networks for Senior Design
Introduction to Neural Networks for Senior Design Intro1 Neural Networks: The Big Picture Artificial Intelligence Neural Networks Expert Systems Machine Learning not ruleoriented ruleoriented Intro2
More informationAN APPLICATION OF TIME SERIES ANALYSIS FOR WEATHER FORECASTING
AN APPLICATION OF TIME SERIES ANALYSIS FOR WEATHER FORECASTING Abhishek Agrawal*, Vikas Kumar** 1,Ashish Pandey** 2,Imran Khan** 3 *(M. Tech Scholar, Department of Computer Science, Bhagwant University,
More informationNeural Network Architectures
6 Neural Network Architectures Bogdan M. Wilamowski Auburn University 6. Introduction... 66. Special EasytoTrain Neural Network Architectures... 6 Polynomial Networks Functional Link Networks Sarajedini
More informationAn Introduction to Neural Networks
An Introduction to Vincent Cheung Kevin Cannons Signal & Data Compression Laboratory Electrical & Computer Engineering University of Manitoba Winnipeg, Manitoba, Canada Advisor: Dr. W. Kinsner May 27,
More informationComputer Organization
Computer Organization and Architecture Designing for Performance Ninth Edition William Stallings International Edition contributions by R. Mohan National Institute of Technology, Tiruchirappalli PEARSON
More informationNeural Networks in Data Mining
IOSR Journal of Engineering (IOSRJEN) ISSN (e): 22503021, ISSN (p): 22788719 Vol. 04, Issue 03 (March. 2014), V6 PP 0106 www.iosrjen.org Neural Networks in Data Mining Ripundeep Singh Gill, Ashima Department
More information6. Feedforward mapping networks
6. Feedforward mapping networks Fundamentals of Computational Neuroscience, T. P. Trappenberg, 2002. Lecture Notes on Brain and Computation ByoungTak Zhang Biointelligence Laboratory School of Computer
More informationInternational Journal of Electronics and Computer Science Engineering 1449
International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN 22771956 Neural Networks in Data Mining Priyanka Gaur Department of Information and
More informationUnivariate and Multivariate Methods PEARSON. Addison Wesley
Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston
More informationOperations Research An Introduction
Operations Research An Introduction Ninth Edition Hamdy A. Taha University of Arkansas, Fayettevilie Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London
More informationBiological Neurons and Neural Networks, Artificial Neurons
Biological Neurons and Neural Networks, Artificial Neurons Neural Computation : Lecture 2 John A. Bullinaria, 2015 1. Organization of the Nervous System and Brain 2. Brains versus Computers: Some Numbers
More informationUsing Neural Networks for Pattern Classification Problems
Using Neural Networks for Pattern Classification Problems Converting an Image Camera captures an image Image needs to be converted to a form that can be processed by the Neural Network Converting an Image
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 informationNeural Network Design in Cloud Computing
International Journal of Computer Trends and Technology volume4issue22013 ABSTRACT: Neural Network Design in Cloud Computing B.Rajkumar #1,T.Gopikiran #2,S.Satyanarayana *3 #1,#2Department of Computer
More informationTolerance of Radial Basis Functions against StuckAtFaults
Tolerance of Radial Basis Functions against StuckAtFaults Ralf Eickhoff 1 and Ulrich Rückert 1 Heinz Nixdorf Institute System and Circuit Technology University of Paderborn, Germany eickhoff,rueckert@hni.upb.de
More informationComputer Science MS Course Descriptions
Computer Science MS Course Descriptions CSc I0400: Operating Systems Underlying theoretical structure of operating systems; inputoutput and storage systems, data management and processing; assembly and
More informationSelf Organizing Maps: Fundamentals
Self Organizing Maps: Fundamentals Introduction to Neural Networks : Lecture 16 John A. Bullinaria, 2004 1. What is a Self Organizing Map? 2. Topographic Maps 3. Setting up a Self Organizing Map 4. Kohonen
More informationIntroduction to the Artificial Neural Networks
Introduction to the Artificial Neural Networks 1 Andrej Krenker 1, Janez Bešter 2 and Andrej Kos 2 1 Consalta d.o.o. 2 Faculty of Electrical Engineering, University of Ljubljana Slovenia 1. Introduction
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 informationAn Introduction to Artificial Neural Networks (ANN)  Methods, Abstraction, and Usage
An Introduction to Artificial Neural Networks (ANN)  Methods, Abstraction, and Usage Introduction An artificial neural network (ANN) reflects a system that is based on operations of biological neural
More informationNeural Networks Kohonen SelfOrganizing Maps
Neural Networks Kohonen SelfOrganizing Maps Mohamed Krini ChristianAlbrechtsUniversität zu Kiel Faculty of Engineering Institute of Electrical and Information Engineering Digital Signal Processing and
More informationHow to Implement Lean Manufacturing
How to Implement Lean Manufacturing Lonnie Wilson Me Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Contents Preface Acknowledgments
More informationAn Introduction to ObjectOriented Programming with
An Introduction to ObjectOriented Programming with TM Java C. Thomas Wu Naval Postgraduate School Ml McGrawHill Boston Burr Ridge, IL Dubuque, IA Madison, WI New York San Francisco St. Louis Bangkok
More informationNONLINEAR TIME SERIES ANALYSIS
NONLINEAR TIME SERIES ANALYSIS HOLGER KANTZ AND THOMAS SCHREIBER Max Planck Institute for the Physics of Complex Sy stems, Dresden I CAMBRIDGE UNIVERSITY PRESS Preface to the first edition pug e xi Preface
More informationCompensating the Sales Force
Compensating the Sales Force A Practical Guide to Designing Winning Sales Reward Programs Second Edition David J. Cichelli Me Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan
More informationIntroduction to Artificial Neural Networks MAE491/591
Introduction to Artificial Neural Networks MAE491/591 Artificial Neural Networks: Biological Inspiration The brain has been extensively studied by scientists. Vast complexity prevents all but rudimentary
More informationPARALLEL PROGRAMMING
PARALLEL PROGRAMMING TECHNIQUES AND APPLICATIONS USING NETWORKED WORKSTATIONS AND PARALLEL COMPUTERS 2nd Edition BARRY WILKINSON University of North Carolina at Charlotte Western Carolina University MICHAEL
More informationPreface. C++ Neural Networks and Fuzzy Logic:Preface. Table of Contents
C++ Neural Networks and Fuzzy Logic by Valluru B. Rao MTBooks, IDG Books Worldwide, Inc. ISBN: 1558515526 Pub Date: 06/01/95 Table of Contents Preface The number of models available in neural network literature
More informationPricing and calibration in local volatility models via fast quantization
Pricing and calibration in local volatility models via fast quantization Parma, 29 th January 2015. Joint work with Giorgia Callegaro and Martino Grasselli Quantization: a brief history Birth: back to
More informationNeural Networks: a replacement for Gaussian Processes?
Neural Networks: a replacement for Gaussian Processes? Matthew Lilley and Marcus Frean Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand marcus@mcs.vuw.ac.nz http://www.mcs.vuw.ac.nz/
More informationLearning. Artificial Intelligence. Learning. Types of Learning. Inductive Learning Method. Inductive Learning. Learning.
Learning Learning is essential for unknown environments, i.e., when designer lacks omniscience Artificial Intelligence Learning Chapter 8 Learning is useful as a system construction method, i.e., expose
More informationENGINEERING PROBLEM SOLVING WITH C++
ENGINEERING PROBLEM SOLVING WITH C++ Third Edition Delores M. Etter Electrical Engineering Department Southern Methodist University, Dallas, TX Jeanine A. Ingber Accurate Solutions in Applied Physics,
More informationENTERPRISE RESOURCE PLANNING
ENTERPRISE RESOURCE PLANNING ~SECOND E DITION~ ENTERPRISE RESOURCE PLANNING ~SECOND E DITION~ Alexis Leon L&L Consultancy Services Pvt Ltd Kochi Tata McGrawHill Publishing Company Limited NEW DELHI McGrawHill
More informationIAI : Biological Intelligence and Neural Networks
IAI : Biological Intelligence and Neural Networks John A. Bullinaria, 2005 1. How do Humans do Intelligent Things? 2. What are Neural Networks? 3. What are Artificial Neural Networks used for? 4. Introduction
More informationMining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group
Practical Data Mining Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor Ei Francis Group, an Informs
More informationPrinciples of Data Mining by Hand&Mannila&Smyth
Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences
More informationSolving Nonlinear Equations Using Recurrent Neural Networks
Solving Nonlinear Equations Using Recurrent Neural Networks Karl Mathia and Richard Saeks, Ph.D. Accurate Automation Corporation 71 Shallowford Road Chattanooga, Tennessee 37421 Abstract A class of recurrent
More informationMachine Learning. 01  Introduction
Machine Learning 01  Introduction Machine learning course One lecture (Wednesday, 9:30, 346) and one exercise (Monday, 17:15, 203). Oral exam, 20 minutes, 5 credit points. Some basic mathematical knowledge
More informationIntroduction To Artificial Neural Network
Introduction To Artificial Neural Network Marcello Pelillo Department of Computer Science University of Venice http://www.unive.it http://www.dsi.unive.it DARPA Neural Network Study Over the history of
More informationData Mining Techniques Chapter 7: Artificial Neural Networks
Data Mining Techniques Chapter 7: Artificial Neural Networks Artificial Neural Networks.................................................. 2 Neural network example...................................................
More informationBack Propagation Neural Network for Wireless Networking
International Journal of Computer Sciences and Engineering Open Access Review Paper Volume4, Issue4 EISSN: 23472693 Back Propagation Neural Network for Wireless Networking Menal Dahiya Maharaja Surajmal
More informationCITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學. SelfOrganizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理
CITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學 SelfOrganizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理 Submitted to Department of Electronic Engineering 電 子 工 程 學 系 in Partial Fulfillment
More informationDepartment of Industrial Engineering
Department of Industrial Engineering Master of Engineering Program in Industrial Engineering (International Program) M.Eng. (Industrial Engineering) Plan A Option 2: Total credits required: minimum 39
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/machinelearning Logistics Lectures M 9:3011:30 am Room 4419 Personnel
More informationAn Artificial Neural NetworksBased online Monitoring Odor Sensing System
Journal of Computer Science 5 (11): 878882, 2009 ISSN 15493636 2009 Science Publications An Artificial Neural NetworksBased online Monitoring Odor Sensing System Yousif AlBastaki The College of Information
More informationChapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining BecerraFernandez, et al.  Knowledge Management 1/e  2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
More informationDistinguished Professor George Washington University. Graw Hill
Mechanics of Fluids Fourth Edition Irving H. Shames Distinguished Professor George Washington University Graw Hill Boston Burr Ridge, IL Dubuque, IA Madison, Wl New York San Francisco St. Louis Bangkok
More informationTRAINING A LIMITEDINTERCONNECT, SYNTHETIC NEURAL IC
777 TRAINING A LIMITEDINTERCONNECT, SYNTHETIC NEURAL IC M.R. Walker. S. Haghighi. A. Afghan. and L.A. Akers Center for Solid State Electronics Research Arizona State University Tempe. AZ 852876206 mwalker@enuxha.eas.asu.edu
More informationChapter 7. Diagnosis and Prognosis of Breast Cancer using Histopathological Data
Chapter 7 Diagnosis and Prognosis of Breast Cancer using Histopathological Data In the previous chapter, a method for classification of mammograms using wavelet analysis and adaptive neurofuzzy inference
More informationRain prediction from meteoradar images
2015 http://excel.fit.vutbr.cz Rain prediction from meteoradar images Michael Vlček t + 1 t + 2... t  2 t  1 t t  3... input layer hidden layers output layer Abstract This paper presents a software
More informationIntelligent Recognition Technology using Artificial Neural Network for Graphics
Intelligent Recognition Technology using Artificial Neural Network for Graphics FAN Bin 1, ZENG XiaoJing *2,a, ZHAO Zhu 3 1,2,3 College of Information technology and Media HeXi University, Zhangye 734000,
More informationADVANCED COMPUTER ARCHITECTURE: Parallelism, Scalability, Programmability
ADVANCED COMPUTER ARCHITECTURE: Parallelism, Scalability, Programmability * Technische Hochschule Darmstadt FACHBEREiCH INTORMATIK Kai Hwang Professor of Electrical Engineering and Computer Science University
More informationSELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS
UDC: 004.8 Original scientific paper SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS Tonimir Kišasondi, Alen Lovren i University of Zagreb, Faculty of Organization and Informatics,
More informationComparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification R. Sathya Professor, Dept. of MCA, Jyoti Nivas College (Autonomous), Professor and Head, Dept. of Mathematics, Bangalore,
More informationLearning is a very general term denoting the way in which agents:
What is learning? Learning is a very general term denoting the way in which agents: Acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality);
More informationRole of Neural network in data mining
Role of Neural network in data mining Chitranjanjit kaur Associate Prof Guru Nanak College, Sukhchainana Phagwara,(GNDU) Punjab, India Pooja kapoor Associate Prof Swami Sarvanand Group Of Institutes Dinanagar(PTU)
More informationThe Process. Improvement. Handbook. A Blueprint for Managing Change and. Increasing Organizational Performance. Tristan Boutros.
The Process Improvement Handbook A Blueprint for Managing Change and Increasing Organizational Performance Tristan Boutros Tim Purdie Illustrations by Dustin Duffy Mc Graw Hill Education New York Chicago
More informationNeural network software tool development: exploring programming language options
INEB PSI Technical Report 20061 Neural network software tool development: exploring programming language options Alexandra Oliveira aao@fe.up.pt Supervisor: Professor Joaquim Marques de Sá June 2006
More information